OrganisationName	Organisation	us	DataProvided	Sensitive	LegalBasisForProvision	OneOff	PatientOptOutsApplied	Objective	ProcessingActivities	ExpectedOutput	ExpectedBenefits
CAPITA	CAPITA	MRIS - List Cleaning Report	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	To distribute, collect, process and submit PROMs questionnaires.
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Hospital Episode Statistics Accident and Emergency	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Hospital Episode Statistics Outpatients	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Hospital Episode Statistics Critical Care	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Office for National Statistics Mortality Data (linkable to HES)	Identifiable	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Bridge file: Hospital Episode Statistics to Mortality Data from the Office of National Statistics	Identifiable	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	One-Off	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	One-Off	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CARE QUALITY COMMISSION (CQC)	CARE QUALITY COMMISSION (CQC)	Mental Health Minimum Data Set	Identifiable	Sensitive	Other-Health and Social Care Act 2012 Schedule 12, Part 9, subsection 11	Ongoing	N	The Care Quality Commission (CQC) is a Non-Departmental Public Body and was established under the Health and Social Care Act 2008. It took on the functions of the Healthcare Commission, the Commission for Social Care Inspection, and the Mental Health Act Commission. CQC has the function of regulating health and adult social care services in England (provided by the NHS, local authorities, private companies or voluntary organisations), and protecting the rights of people detained under the Mental Health Act. CQC ensures that health and social care services provide people with safe, effective, compassionate, high-quality care and encourages them to improve. CQC use these data to populate indicators within their Intelligent Monitoring (and forthcoming) CQC insight tools, which help CQC focus inspections of providers by deciding when, where and what to inspect. These data are also used to construct data packs (and forthcoming evidence tables) which bring together information and analysis for each provider and are used for inspection planning. These data are also used within the applicants Outliers programme to identify when key metrics relating to mortality rates and maternity outcomes reach a level that may warrant further investigation. In addition, CQC use the information in thematic reviews and national reporting; for example the Annual State of Care report. It is occasionally necessary to identify to a trust examples of their own patients whose care appears problematical. A single field - local patient identifier - is used as the HESID is not known to the trust. With this exception, no record level data is released to third parties. CQC uses identifiable data in relation to its code of practice as per the below – Under the 2008 Act (amended 2012), CQC is responsible for the regulation of: - treatment, care and support provided by hospitals, GPs dentists, ambulances and mental health services. - treatment, care and support services for adults in care homes and in people’s own homes (both personal and nursing care). - services for people whose rights are restricted under the Mental Health Act. CQC monitor, inspect and regulate services to make sure they meet fundamental standards of quality and safety and it publishes what it finds, including performance ratings to help people choose care. CQC’s use of identifiable data is subject to our statutory Code of Practice on Confidential Personal Information (the code). The purpose of the code is to provide transparency on our use of information for data subjects and other stakeholders and as a guide for staff on the practices we will follow. The code is a requirement of the Health & Social Care Act 2008 which itself creates safeguards on our use of information in addition to the requirements of the Data Protection Act 1998 and compliance with other relevant legislation and common law duties (as detailed in the code appendix). Of particular relevance to this application is the CQC's first ‘principle’ - "governing our use of information." “We will only obtain confidential personal information where it is necessary to do so for the purpose of exercising our functions”. The CQC are satisfied the substance of this agreement meet this requirement. All use of confidential personal information by CQC is in accordance with the ‘necessity test’ which informs the CQC's decision on obtaining, using or disclosing. This test is the foundation for the code and is designed to ensure the minimum processing is performed. Of relevance to this application, the CQC ensure that access to the requested identifiable data is limited to the small team of whom it is necessary to process these data to produce non-identifiable outputs for internal CQC use. This is in accordance with the CQC's fourth principle, that "We will use only the minimum necessary confidential personal information. We will use anonymised information wherever possible…” It is also CQC policy to extend the application of the Code to information relating to the deceased, providing further safeguards to the use of identifying data (Principle 7). A number of identifiers are required for matching, to permit records for individual patients to be linked consistently both across and within datasets. These identifiers are postcode of patient, and local patient identifier. The local patient identifier is also used where there is a patient whose care appears problematical. In this instance the local patient identifier can be sent to the originating Trust to assist with the investigations. In addition, the patient’s postcode is used for assigning data to new PCT/LA/CCG boundaries, and for identifying other geography-related data (such as linking with deprivation tables, calculating distances from the address to the Trust, to help monitor for adverse events for people living in care homes). LD Census Data: CQC has a current agreement for access to HES and MHLDDS associated data. It is seeking access to LD Census that is, in many facets, a predecessor dataset to MHLDDS; many items within the LD Census (approx. 3000 records per year) were subsequently incorporated within the MHLDDS. A key mandate for the LD Census collection (Section 2.5) stipulated that everyone with long-term conditions, including people with mental health problems, will be offered a personalised care plan that reflects their preferences and agreed decisions. CQC will use these data to measure their implementation through one or more indicators relating to care plan provision within learning disability services. MHLDDS-ONS linked data: CQC has a current agreement for access to HES and MHLDDS associated data (including the HES-ONS linked mortality file). CQC are aware that there exist some community patients who do not have a HES record. In order to ensure that mortality analyses are able to cover fully all patient groups, CQC therefore request access to a MHLDDS-ONS linked data. This dataset would be used for the same objective as HES-ONS data within the current agreement. Addition of Mental Health Services Data Set (MHSDS) (and historic KP90) data: MHSDS is a patient level, output based, secondary uses data set which delivers robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with Mental Health Services. It covers not only services provided in hospitals, but also in outpatient clinics and in the community, where the majority of people in contact with these services are treated. MHSDS brings together key information from Adult and Children's mental health, learning disabilities or autism spectrum disorder, CYP-IAPT and early intervention care pathway that has been captured on clinical systems as part of patient care. Some of the information included in MHSDS was previously collected as the KP90 dataset. The information in MHSDS (and KP90) provides key information to the work CQC carries out, such as making sure providers have effective systems and processes to meet the Mental Health Act 1983.	Only staff working on the specific purposes below can access the data and only for the purpose specified. Atos is CQC’s ICT service provider and was engaged through the IMS3 framework contract, let by the Department of Health. The data from NHS Digital will only be processed by substantive employees of CQC and ATOS (note that the local patient identifier may be shared with the originating Trust to identify patient notes to review, as detailed below in the Benefits section). The identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. Regarding the use of the data, HES, HES-ONS, HES- MHLDDS (using the MHLDDS Bridging file), MHLDDS, KP90, and MHSDS data are loaded into statistical packages and their outputs are used for the population of Intelligent Monitoring/CQC insight, individual data packs/ evidence tables, outlier analysis, thematic reviews, and national reporting products to support the objectives above. The outputs have small numbers suppressed in line with the HES Analysis Guide. Use/processing of the ONS data is subject to ONS Terms and Conditions. CQC presently identify the sources of data and the calculation methodologies for each of the products. In order to publicise more clearly to the public how CQC uses identifiable data, the CQC Board has approved our Information Governance strategy that requires CQC to conduct a review of the information it collects and uses directly/indirectly from the public and how this, in turn, is communicated more widely. This review will build on current moves(for example, review of published leaflets) to be more transparent about how such information features in our regulatory programme. LD Census Data: As above, the identifiable data will be loaded into a separate database where it will be analysed by a small team (currently 6); this is the only team that can access the identifiable data. They will analyse the data and, where sufficient numbers are present within the data to maintain confidentiality, will provide summary counts only for providers to be included within data packs/ evidence tables (see categories below): - Data totally suppressed as total below 6 - Data totally suppressed as total below 6 when split by gender - Data not provided for inclusion in data pack/ evidence table as total below 25 when split by gender (though internal discussions may take place on the results) - Data suitable for presentation and incorporated within data pack/ evidence table or supporting materials In order to help to understand the appropriateness of extended lengths of stay for particular conditions, it is necessary to be able to track an individual across more than one LD census. Reference will also be made to the appropriateness of the locations’ registration details (regulated activities). This is not designed to identify the individual but to ascertain an indicator of quality of care at a given location. The tracking would involve the need to link with the HES/MHLDDS bridging file to understand if records for that individual were present in MHLDDS. Where present, the MHLDDS should confirm the LD Census return. Where no record/appropriate record is present in MHLDDS, CQC would follow the pathway in HES to seek to identify the presence of any adverse event; for example unusual admittance to hospital as referenced in Winterbourne View. Such monitoring of potential adverse events may have a significant effect on detecting and recognising providers of poor and harmful care. For this purpose, CQC would seek agreement to link the LD Census return with HES/MHLDDS in this way. MHLDDS-ONS linked data: As above, the identifiable data are loaded into a separate database which is analysed by a small team (currently 6); this is the only team that can access the identifiable data. They analyse the data and provide cuts - pseudonymised at both record and provider level - for statistical packages for use in the Intelligence Directorate only by identified staff. These staff request the bespoke breakdowns from the analytical team. The MHLDDS-ONS linked data would be processed in line with the HES-ONS linked mortality data set out above.	The on-going outputs produced are: Risk-based Monitoring (Intelligent Monitoring and CQC insight): outputs are published on the internet as indicators and overall provider ranking. This is aggregate information with small numbers suppressed. Data and information packs/evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. For example, 'Same-day procedure' indicator within HES sets out the proportion of elective admissions where the patient’s primary procedure was on the day of admission and provides high-level information to the inspection team for planning/ review purposes. Outliers Programme: analysis of certain metrics derived from these data are shared with trusts who have hit a statistical threshold that is regarded as being of concern. A trust receives analysis only of patients admitted at that trust with a comparison to overall national rates. For example, indicators have been generated on ‘Perinatal mortality and neonatal readmissions’ used to monitor the care of new-borns; 'therapeutic endoscopic procedures on biliary tract' raised in response to a Dr Foster alert - investigated death rates for endoscopic procedures. National Reporting: used to support the creation and population of national reports providing a qualitative review of health and care supported by aggregated information. The nature of these reports varies year on year depending on CQC's topics of interest. This is aggregate information with small numbers suppressed. For example, MHLDDS was used within CQC’s annual ‘Monitoring the Mental Health Act’; the 2014/15 publication appeared in December 2015. Thematic reviews: used internally to inform CQC's regulatory activities. They provide an in depth review of selected areas of care, for example dementia care. This is aggregate information with small numbers suppressed. A further example being a review of urgent care in which the indicator of ‘death within 30 days of stroke, neck of femur, acute myocardial infarction’ was used from HES. They are shared publicly. In the monitoring of organisations for ongoing compliance against essential quality and safety standards, CQC uses screening techniques in its Intelligent Monitoring/CQC insight tools, which analyse a wide variety of data sources to highlight possible outlying concerns that trigger actions where concerns are raised. Such screening methods are aided by a more local approach to information gathering and analysis, which is being developed in consultation with appropriate stakeholders. These techniques, currently along with national surveys of patients, help to create a more holistic understanding in informing its work with ongoing compliance, investigations, and thematic reviews. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. LD Census Data: Data and information packs/ evidence tables: used by the inspections teams and shared with providers. This is aggregate information with small numbers suppressed. Data packs/ evidence tables contain analyses from a number of different sources and are produced as pre-inspection material designed to aid the planning of inspections at individual CQC locations. Where adverse events may be detected, an internal review of the findings would be required that could either feed into individual data packs/evidence tables or in determining the need to bring inspections forward. National reports (for example, State of Care Report). High-level analyses with aggregate information with small numbers suppressed. MHLDDS-ONS linked data: This dataset will be used in conjunction with the HES and MHLDDS associated data in producing the list of outputs stipulated above. The data provides a crucial insight into mortality in mental health settings. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).	In the applicant’s monitoring they prioritise the care and welfare of patients. People who use services should experience effective, safe and appropriate care, treatment and support that meets their needs and protects their rights. CQC uses these data to help determine five core questions about services: - Are they safe - Are they effective - Are they caring - Are they well-led? - Are they responsive to people’s needs? HES, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS data are used to determine key performance indicators in the Intelligent Monitoring/CQC insight products. Each indicator is categorised in one of the core domains that, in total, provide a view for both the public and clinicians on the quality of the provision of care. In addition to current indicators, CQC have used HES and MHLDDS data over the course of previous months in running models to determine the shape and content for CQC Insight (successor to Intelligent Monitoring); the driver being to improve the quality of information available in determining a risk-based monitoring approach, The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. Each data pack uses the data (HES and/ or MHLDDS as appropriate) both to present an overview of the core business of the trust in terms of activity – helping to determine the specialist requirements of the inspection team - while also including specific metrics (eg. HES re-admissions as descriptive statistics). HES and HES-ONS data are used in the outliers programme. Outlier events, such as high mortality or readmission rates may be identified either by CQC's own analyses or by those of Dr Foster. For both, CQC gathers all available information - including HES analyses, where appropriate, and advice from experts - and presents this to an internal review panel. This panel decides whether or not CQC investigate. When CQC decide to proceed, the data is shared information with the trust for them to review and comment upon. One of the review actions a trust may carry out is a case note review. On rare occasions the trust is unable to reconcile HES counts with their local systems. In those cases CQC may share a small number of values for the local patient identifier (lopatid) with the trust so they can identify patient notes to review. The mortality panels meet monthly while the maternity panels meet every two months. Where outlier concerns are identified, the subsequent contact and engagement with trusts have led to implementation of improvements in process that have had a positive impact on patient care. HES, HES-ONS, HES-MHLDDS (using the MHLDDS Bridging file) and MHLDDS are used in analyses for thematic reviews and in the development of new Intelligent Monitoring/CQC insight indicators; for example, in reviewing quality of access to care across different ethnicities. Thematic reviews provide in-depth analyses of chosen topics to inform CQC staff, clinicians and the public about that service/area of care while also highlighting areas for improvement/best practice; working with inspector colleagues, themed inspections allow CQC to develop recommendations for making improvements in the delivery of care. These recommendations are then incorporated into future inspections to encourage continual improvement. ‘End of Life Care’ and ‘Safety in hospitals’ are examples of thematic data reviews/themed inspections that took place during the period of the current agreement that helped to raise specific questions on the monitoring and provision care in these areas with a focus on future improvement. Intelligent monitoring (and move to CQC insight) is updated at least once a quarter. Outlier analyses are undertaken on a monthly or bi-monthly basis. Data packs/evidence tables are created on an on-going basis for impending inspections. National reporting is a combination of predominantly annual reports as well as topic-specific reports released on a one off basis. Addition of LD Census Data: The pre-inspection data packs/evidence tables help the planning and review stages of an inspection that seeks to highlight areas of poor care requiring improvement while also seeking to promote good practice. In addition, the data will also be used in the CQC’s remit to investigate serious concerns about the quality of public services. These data will be a vital pillar in both a national system of monitoring registered organisations, and the development and publication of reliable performance indicators. CQC is tasked with measuring improvement in the health and social care provision. The ability to focus on the difference between the cohort incorporated within multi-year LD Census and the remaining patient population provides a real comparator against changes in healthcare outcome within the wider HES/MHLDDS datasets. General profiling of users within individual locations of care will help the inspection team in planning the type of inspection and requirements in terms of inspection team members – ensuring appropriate skills-base to carry out optimum inspections. They will also provide the inspection team with pertinent questions about the optimum length of stay for the cohort of users. The analyses and data packs/evidence tables also assist with the identification of providers operating illegally/ contrary to registration requirements. This will lead to follow up action by the registration team and consideration of enforcement action to counter and remove any illegal provision of care. This ensures vulnerable users are protected from poor provision of care. MHLDDS-ONS linked data: This dataset will be used alongside HES, MHLDDS and HES-ONS in creating the tangible benefits listed above. In particular, it will enable CQC to ensure that no community mental health users (without a HES record) are excluded from linked mortality analyses and, in so doing, help CQC develop a more holistic understanding of quality within a mental health setting. Addition of KP90 and MHSDS data: These datasets will be used in conjunction with the HES, ONS, and Mental Health data to help in producing the outputs listed above. The data will be particularly useful in relation to Mental Health (including detentions under the Mental Health Act).
CHILDREN'S COMMISSIONER'S OFFICE	CHILDREN'S COMMISSIONER'S OFFICE	Mental Health Services Data Set	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	One-Off	N	The Children's Commissioner's Office requires a pseudonymised extract from the Mental Health Service Data Set (MHSDS) to conduct a review of distances travelled by children to Tier4 Child and Adolescent Mental Health Services (CAMHS) inpatient units. This project is being undertaken as part of the Children’s Commissioner’s Business Plan for 2017-18 and supports The Children’s Commissioner’s statutory roles to promote and protect children's rights with particular focus on vulnerable groups. Specifically, the Children’s Commissioner requires this data given that it has responsibilities to promote the following rights under Sections 2(1), 2(3) and 2(4) of the Children’s Act 2004 (as amended): • Section (2)1 – promote and protect Children’s rights to healthcare as set out in the UN Convention of the Rights of the Child • Section 2(3) - duty on the Commissioner to “consider the potential effect on the rights of children of government policy proposals and government proposals for legislation” • Section 2(4) - responsibility towards children living away from home. “In the discharge of the primary function, the Children’s Commissioner must have particular regard to the rights of children who are within section 8A (children living away from home or receiving social care) and other groups of children who the Commissioner considers to be at particular risk of having their rights infringed.” A key aim of the project is identifying instances where children are having to travel ‘out of area’ (i.e. excessively long distances from home) to receive access to care so that these shortfalls can be addressed. A particular focus is on variation (if any) by area and children's characteristics. A key issue is that while the NHS has a current definition of ‘out of area’ for adults but not for children. This project will perform an equivalent assessment for children. Prior discussions with clinicians have indicated that this will be a sensitive issue to tackle with the challenge being to balance the need for children to receive care sufficiently close to home while also having access to appropriate specialist care. Key findings will be published in a report, used for briefings and evidence submissions.	NHS Digital will produce an extract of record level pseudonymised data from the MHSDS appropriately filtered to meet the specific requirements of this analysis. No personal data will be used. The information requested is primarily focused on admissions rather than on individual patients. The data will be stored on the Department for Education secure network on a secure folder with restricted access - only accessible to certain individuals through password-protected staff accounts on password-protected devices. The data will be accessed only by individuals within the Children's Commissioner's Office Evidence Team, all of whom are substantively employed by the Children’s Commissioner’s Office. All Children’s Commissioner staff members have information security training and enhanced DBS checks. The data will not be used for any other purpose beyond the assessment of Tier 4 CAMHS Children’s Units. The raw data will not be shared with any third parties. Any reports made available outside of the Children’s Commissioner’s Office will be compliant with the MHSDS disclosure control rules including suppression and rounding. Dependent on the findings, the Children’s Commissioner’s Office might want to discuss their analyses with other parties in a more granular level which may involve disclosure of low cell counts. These parties will be limited to the Department of Health, the Care Quality Commission (CQC) and the Royal College of Psychiatrists. Should the need arise, these would be private discussions hosted by the Children’s Commissioner’s Office. Every care will be taken to protect the anonymity of individuals. No third party will be given access to raw data and third parties will not be able to take any information away from the discussion that could put patient confidentiality at risk.	The planned outputs are: 1. A short policy report focussed on the findings and recommendations following the analysis 2. Briefings 3. Press releases and associated media activity These outputs are expected to be complete by October 2017. Findings will be reported to the Department for Health ahead of its green paper. A version of findings may be additionally published in a format targeted to a younger audience. A summary of the project and its findings will be included in the Children’s Commissioner’s annual report to parliament. Findings may also be shared with other public bodies in the event that public bodies have a legitimate interest and request evidence submissions. Outputs will contain no raw data and will contain only derived information in the form of aggregated statistics, tables, graphs and visualisations. All outputs will be compliant with MHSDS disclosure control rules including suppression and rounding.	The information is required to support the Children’s Commissioner’s statutory remit to promote awareness of the views and interests of all children in England. The findings will be used to inform policy makers around health and social care, ensuring policy for Tier 4 CAMHS is evidence-based and of benefit to children and young people with mental health conditions. The ultimate benefit from this work will be to identify where more hospitals beds and services are needed to stop children having to go a long distance from home. This analysis will highlight where those shortfalls are and the dissemination of the findings will contribute towards those issues being addressed.
CHKS LIMITED	CHKS LIMITED	Office for National Statistics Mortality Data	Anonymised - ICO code compliant	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	Ongoing	N	The Data Recipient agrees to process the Data only for the following two purposes agreed with NHS Digital: Objective for processing: To produce/analyse statistics using deaths data solely to help the NHS perform its duties. CHKS will process the SHMI data, associated HES APC data, ONS mortality data for the services listed below: • Benchmarking service for NHS Providers (Trusts) and Commissioners (CSUs and CCGs) – data are processed into a comparative database used to provide indicator level benchmarks both online and in offline reports. • Mortality profiling service for NHS Trusts – data processed and accessible at record level in pseudonymised form by individual client site only. The data will not be used for anything additional to the above purpose’s any further use of the data will be subject to an amendment of the agreement.	SHMI indicator data provided are processed using proprietary data processing software which analyses, cleanses, groups, and outputs the data into service-based patient-level databases. Any data are held only in pseudonymised form. The ONS mortality data will be linked to HES data to allow analysis of out-of-hospital deaths. Data will never be directly linked with other datasets which could allow re-identification of individuals. The CHKS live secure online system which is one of the outputs is held on CHKS servers. The servers are stored in Six Degrees Group datacentres which are located in England. Processed data is loaded to these servers by CHKS. Six Degrees Group do not have access to any of the outputs or data and are not involved in processing data. To minimize the amount of data held CHKS uses a rolling five years (plus year to date) data to produce the outputs required. This is to allow sufficient historic comparison of past performance. As such CHKS would only be looking to retain data in this rolling period and will periodically delete any data held from before this period and return a certificate of destruction as required.	Specific outputs expected, including target date: Data will only be used in processed form in solely the following outputs: • CHKS live – this is a secure online portal which is accessible by authorised and authenticated users at CHKS client Trusts/CSUs/CCGs and authorised and authenticated CHKS staff. Users access the data through a range of indicator dashboards and scorecards presented at aggregate level. The online benchmarking and mortality profiling services are all accessible through the portal. Each client organisation is only given access to the specific services for which they have contracted; • Bespoke reporting – electronic or hard copy reports provided to NHS Trusts, CCGs or CSUs, providing analysis and commentary on trends in healthcare. All small numbers are suppressed in reports. It is anticipated that the above outputs will be available for use by contracted organisations within approximately 2 weeks of data being received by CHKS. Using the ONS mortality data CHKS will be able to generate summary indicators which take into account out of hospital mortality. It is anticipated that these indicators will be available within approximately 2 months of data being received by CHKS. The Mortality profiling service will display Trusts HES data for their own specific activity. Trusts can drill down to record-level for their activity for the purposes of audits and to allow NHS Trusts to review mortality cases and monitor and improve patient care (the fields that will be available in this drill down are – Admission Date, Discharge Date, Date of Death, Method of Admission, Method of Discharge, Age, Sex, Risk Prediction, Primary Diagnosis, Secondary Diagnosis). The only patient-level data field which is potentially identifiable is Date of Death (when linked with the other available fields this may make the patient record identifiable, however the Trust will only have access to data pertaining to their own patients). None of the data is linked to any client-submitted data (CHKS will not combine SHMI data supplied under this agreement with any other data that CHKS holds, other than data in the public domain) but provides information on diagnosis codes to allow meaningful audit of key conditions. No form of patient ID (HES ID) or consultant ID is included in the record-level data available to NHS Trusts. No individuals, doctors, consultants, or patients are ever identified in CHKS products, systems, or reporting using data provided by NHS Digital. Data are held in the above outputs only in pseudonymised or anonymised form. Record level raw data are never made available to any third party other than where stated in this application (where NHS Trusts can access their own activity in CHKS tools). Whilst CHKS is part of the Capita Group, data are only used by CHKS for the purposes above and not shared with other organisations within the Capita Group. CHKS displays a HES data statement as per NHS Digital requirements which currently says: “HES data re-used with the permission of The Health and Social Care Information Centre. All rights reserved.” This can be updated to “HES data re-used with the permission of NHS Digital. All rights reserved.”	Expected measurable benefits to health and/or social care including target date: CHKS is currently contracted to provide services to 92 NHS organisations within England, Scotland, Wales, and Northern Ireland with contracts extending into 2018 with the primary benefit to improve patient care within the NHS. CHKS contract renewals rates within the last year are approximately 90%. The service provided by CHKS provides assurance for Trust boards and demonstrates NHS organisational commitment to continuous improvement. The services support internal analysis of performance, provide evidence for targeting improvement, demonstrate trends over time and progress made in priority areas, compare Trust performance against local targets and national peers, and engage users across client organisations. The primary benefit of using the SHMI data and the HES-ONS linked mortality data is that it enables NHS organisations to identify areas where reductions in mortality may be made and to ultimately improve the quality of patient care. CHKS anticipates being able to provide case study examples of how SHMI data are used, to support future applications. The HES-ONS linked mortality data will be used to allow analysis of both in, and out of, hospital mortality. This data will allow NHS organisations to identify particular case types where patients are dying after discharge from hospital, and identify areas where changes can be made to reduce mortality.
CHKS LIMITED	CHKS LIMITED	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	CHKS Limited aims to produce/analyse statistics using HES data to help the NHS perform its duties. Data provided are only used by CHKS for the purposes, activities, and outputs defined in this agreement. CHKS Limited uses HES data to support and indirectly improve the provision of patient care by healthcare organisations and supporting NHS functions in England, Scotland, Wales, and Northern Ireland. NHS organisations using CHKS services benchmark and compare themselves against both national and local peers dependant on the casemix and provision of activity therefore a national dataset is required to allow such benchmarks to take place. Typically an NHS organisation will select a range of comparative providers from the national dataset, however some NHS organisations also wish to benchmark against a national acute non-specialist provider peer. In addition CHKS services allow NHS organisations to interpret and analyse national indicators, such as HSMR and SHMI, which are available at a national level. CHKS has been providing similar services to NHS organisations for over 25 years. CHKS Limited’s use of the HES data is restricted to the following: 1. Benchmarking of services for NHS providers (NHS Trusts, Local Health Boards) and NHS commissioners (CCGs,CSUs, and Local Health Boards) where data are used for creation of indicators and peer groups and are made available through an online tool and in reports; 2. Market share analysis services for healthcare providers (NHS Trusts) and commissioners (CCGs, and CSUs); 3. Data analysis toolkit services for healthcare providers (NHS Trusts, Local Health Boards) and commissioners (CCGs, and CSUs); 4. Mortality profiling service for NHS Trusts to review mortality; 5. Consultant appraisal services for NHS Trusts; 6. CHKS national Top Hospital awards celebrating success for NHS Trusts; 7. ‘Learning from the Best’ case studies for NHS organisations; 8. Providing a one-off set of aggregated indicators for the British Association of Day Surgery (BADS) Directory of Procedures for NHS providers.	HES data provided are processed using proprietary data processing software which analyses, cleanses, groups, and outputs the data into service-based patient level databases. Any HES data are held only in pseudonymised form and are never directly linked with other datasets which could allow re-identification of HES data. As well as HES, CHKS Limted process other datasets (directly submitted patient data, and publicly available datasets – PLACE, SHMI, RTT,Friends & Family Test, PROMS, Reference Costs, Staff Survey, Patient Survey, Cancer Waits, CDIFF/MRSA, Safety Thermometer, CQC Intelligence Monitoring). These other datasets are not directly linked to HES but are available as indicators. A user could view an indicator derived from HES data (e.g. Average LoS, Mortality) on the same screen as indicators derived from the other datasets mentioned. Using the patient level databases held CHKS Limited generates different sets of benchmarks, indicators, risk models, peer groups, and records which are then used for the services defined below: 1. Benchmarking service to NHS providers and NHS commissioners – HES data are processed into an aggregated comparative database used to provide indicator level benchmarks both online and in reports. 2. Market share analysis services – HES data are processed into an aggregated provider or commissioner based comparative market share databases. 3. Data Analysis toolkit service – HES data are processed into a pseudonymised patient database on which NHS providers and NHS commissioners can run summary peer reports. 4. Mortality profiling service – HES data processed and accessible at record level in pseudonymised form for client site only. 5. Consultant appraisal service – HES data processed and used to create aggregated consultant peer groups for comparative analysis. This is the only activity that utilises the Consultant Code field. 6. CHKS national Top Hospital awards – HES data are processed and used to create aggregated indicators held at Trust level. 7. NHS case studies – uses the aggregated comparative HES database also used for the benchmarking services; 8. BADS Directory of Procedures supplement - uses the aggregated comparative HES database also used for the benchmarking services. This has now been delivered and published as of October 2016. CHKS use 5 'core' years of full data, plus year to date to generate their outputs. An additional year is retained purely to allow Spells which ended in the earliest year to be generated (for example, spells which ended in 2011/12 but started in 2010/11, resulting in the retention of the 2010/11 purely for this purpose until it is superceded). This is to allow sufficient historic comparison of previous years performance. Once the 2016/17 annual refresh data have been received and processed CHKS will delete the 2010/11 HES data.	HES data will only be used in processed form in solely the following outputs: 1. CHKS live – this is a secure online portal which is accessible by authorised and authenticated users at contracted CHKS client sites and authorised and authenticated CHKS staff. Users access the data through a range of indicator dashboards and scorecards presented at aggregate level. The benchmarking, market share analysis, data analysis toolkit, mortality profiling services, and consultant appraisal services are all accessible through the portal. Each client organisation is only given access to the specific services for which they have contracted. All users accessing CHKS live are informed they are required to comply with the HES Analysis Guide; 2. Consultant appraisal reporting – electronic or hard copy reports provided to NHS Trusts providing analysis of consultant performance for appraisal. HES data used are summarised and non-identifiable and used in peer data only. Consultant benchmarks are reported independently and are not linked to individual sites. The service uses the pseudonymised consultant identifier to aggregates of Finish Consultant Episodes data, in order to show relative workload and performance indicators for consultants in peer hospitals. This is reported at anonymised and aggregated level with no patient level drill down. No other detail of consultant activity is reported. 3. Bespoke reporting – electronic or hard copy reports provided to NHS Trusts, or recognised NHS functions, providing analysis and commentary on trends in healthcare. The data will not be released outside the NHS. All small numbers are suppressed in reports in accordance with the HES Analysis Guide. 4. National awards – Trust-level aggregated indicators based on quality, improvement and best practice, and are used to determine top performing NHS organisations. Awards are held on annual basis in May. 5. Case studies – electronic or hard copy reports provided to NHS organisations. Data are provided at aggregate level only and all small numbers are suppressed. 6. BADS Directory of Procedures – National Dataset to publish alongside the guide/directory produced and published by BADS which includes the target for procedures agreed by BADS. The National dataset supplement includes data that reflects outcomes for England, with planned management intent for day surgery, and is divided into cohorts showing the percentage of procedures successfully carried out on a day case basis. Included for each procedure are aggregated indicators reporting on the performance of the top 5%, 25% and 50% of hospitals with each operation. All data is aggregated to national level and published with all small numbers suppressed. This has now been delivered and published as of October 2016. Additional Information on the above outputs • CHKS live services containing HES data are used to provide indicator and peer level comparisons in aggregated form • Within the benchmarking service NHS providers can access pseudonymised and non-sensitive record-level data for their own activity to allow providers to review benchmarks at a granular level, however all peer comparisons are at aggregated and summarised level. NHS commissioners can only view aggregated and summarised indicator level benchmarks and cannot drill down to record-level data. • The Mortality profiling service allows NHS providers to access HES data for their specific activity where data is available at record level for the purposes of audits and review to allow NHS trusts to review mortality case and monitor and improve patient care. This data are not patient identifiable and is not linked to any client submitted data but provides information on diagnosis codes to allow meaningful audit of key conditions. • The Data Analysis Toolkit only allows NHS providers and NHS commissioners to see HES data aggregated in peer based reports. Users within the Data Analysis Toolkit create a tabulation by selecting from a range of available fields – the source data is at record level and the Data Analysis Toolkit then aggregates the data based on the fields the user selects. The user is then presented with the aggregated report and they do not see the record level data used to generate the tabulation. Peer based reports do not include Patient ID or Consultant ID fields. Users can download peer based reports. All users of DAT are required to accept a condition requiring adherence to the HES Analysis Guide before being permitted to run or download a Peer based report. • NHS organisations accessing these services do not have access to the HES Local Patient Identifier or the HES Consultant Identifiers through CHKS services. • Electronic or hard copy reports are provided to NHS Trusts providing analysis of consultant performance for appraisal. Consultant Code will be used in Consultant Appraisal reporting to allow consultant appraisal reports to contain activity carried out by the consultant at other NHS Trusts. This is currently not possible using pseudonymised consultant code. • The appraisal reports are made available directly to the named consultant in each trust or to the appraisal manager/Coordinator/revalidation responsible officer or medical director in the Trust where the consultant’s main contract is held. consultant’s work can be seen in other trusts but in summarised and aggregated form and not at patient level – the consultant report summarises activity, length of stay, day cases rates, complications, readmissions, and mortality indicators. • Consultant reports will not be made available to the public by CHKS and will solely be provided to NHS Trusts that are clients of CHKS. • The clear consultant code field will only be used for the Consultant Appraisal objective, processing and outputs. Other relevant supporting information: No individuals, doctors, consultants, or patients are ever identified in CHKS products, systems, or reporting using data provided by NHS Digital. HES data are held in the above outputs only in pseudonymised form and are never associated with other datasets held in CHKS systems. Record level data are never made available to any third party organisation unless specifically stated. Whilst CHKS Limited is part of the Capita Group only aggregated data are only used by CHKS Limited for the purposes above and not shared with other organisations within the Capita Group. CHKS displays a HES data statement wherever HES data are used. The statement says: “HES data re-used with the permission of The Health and Social Care Information Centre. All rights reserved.” This statement is present on all CHKS live pages, any extracts downloaded from CHKS live, and all bespoke consultancy reports. The CHKS live secure online system is held on CHKS Limited servers. The servers are physically stored in a Six Degrees Group datacentre which is located in England. Processed record-level HES data is loaded to these servers by CHKS. Six Degrees Group do not have access to any of the outputs or data; they provide physical locations to host the servers and network infrastructure but the servers are exclusively managed and used by CHKS Limited.	CHKS is currently contracted to provide the above described to around 80 NHS organisations within England, Scotland, Wales, and Northern Ireland with contracts extending into 2018 with the primary purpose to improve patient care within the NHS. CHKS contract renewals rates within the last year are approximately 75%. The service provided by CHKS provides assurance for trust boards and demonstrates NHS organisational commitment to continuous improvement. The services support internal analysis of performance, provides evidence for targeting improvement, demonstrate trends over time and progress made in priority areas, compare trust performance against local targets and national peers, and engage users across client organisations. NHS organisations are using CHKS services to: • Improve the quality of care; • Increase efficiency; • Increase productivity; • Monitor and reduce mortality; • Improve patient safety; • Reduce length of stay; • Reduce costs by analysing admissions; • Reduce readmissions; • Improve data quality; • Monitor, analyse, and understand commissioning; • Understand service users, populations, and providers; • Plan services; • Manage risks; • Improve utilisation; • Respond to regulatory requirements. Realisation of these benefits is ongoing however to support the usage of NHS Digital supplied data CHKS has made available case studies. These case studies include: • Royal Surrey County NHS Foundation Trust, who have used CHKS benchmarking tools and achieved improvements to patient safety. This was managed through the creation of indicators and benchmarks against length of stay, complications, misadventures, and mortality. Improvements to data quality were also realised. • Mid Cheshire Hospitals NHS Foundation Trust, who have used CHKS benchmarking tools and risk adjusted mortality models to identify areas where mortality indices were high and then take steps to improve the quality of care and reduce mortality. • North East London CSU, who have used CHKS benchmarking tools and national HES data to achieve improvements in provider productivity by using benchmarked data to set targets for acute Trust providers. Full case studies and more information can be found on the CHKS website at http://www.chks.co.uk/Knowledge-Base. In addition feedback from NHS organisations includes (those marked * were delivered in the second half of 2016): • a provider in the South West uses CHKS benchmarking tools where HES data is used to generate comparative metrics for Mortality where the provider delivers specialist care The output from these metrics feeds into a Quality Intelligence Group chaired by the Medical Director which identifies issues across the provider, feeds back to the appropriate departments, and monitors ongoing performance, therefore improving patient care; • a provider in the Midlands uses CHKS benchmarking tools where HES data is used as both a national peer and as a pre-defined peer of clinically similar organisations to review performance using a suite of indicator scorecards. Output from these scorecards is reviewed at board level and by review groups within the trust and fed back to clinicians to help improve patient care. • a large Trust in the Midlands receives a quarterly reporting pack derived from CHKS benchmarking tools covering a range of key indicators, including mortality, readmissions, length of stay and quality indicators (using national and quality account HES peers) which is used by the Trust to monitor improvements and highlight outliers with the clinical directorates. * CHKS Limited introduced a new commissioner based benchmarking and analysis tool in 2016, which is being used to support commissioning organisations in England. The tool allows commissioners to view benchmarked indicators across a range of key reporting areas. In addition a number of new population standardised indicators are available including Total Spells, Total OP Attendances, Total A&E Attendances, Admitted Bed Days, Readmissions, Unplanned Hospitalisation, Emergency admissions for acute conditions that should not usually require hospital admission, and Emergency admissions for children with Lower Respiratory Tract Infections (LRTIs) that should not usually require hospital admission. These new indicators provide observed and standardised expected values allowing commissioners to understand performance for their population. CHKS would anticipate reporting further benefits at a future renewal. CHKS Limited’s request for clear Consultant Code (consult) data item, for use in NHS consultant appraisal, will add further benefits as follows. Medical appraisal has been a requirement for consultants since 2001. Medical appraisal is used to support the delivery of a safe, committed, compassionate, and caring service to patients, help supervise and support doctors, and support the process of medical revalidation (Source: NHS England Medical Appraisal Policy). The addition of clear consultant code will allow CHKS Limited to provide better information to support consultant appraisal where consultants work for more than one NHS Trust. Currently those consultants who work across more than one trust are unable at present to see aggregated data in one report unless both trusts happen to be a client of CHKS. In addition many consultants now move throughout their consultant career and may often wish to have access to multiple site data which CHKS Limited have in HES but needs the Consultant Code to identify the consultant to provide trended aggregated information on performance case mix or workload. Allowing this will mean that NHS Trusts can see performance for new consultants at their first appraisal rather than relying on limited information from a few months’ work and so improving the appraisal process. Improvements to consultant appraisal will ultimately allow NHS Trusts to ensure their consultants are delivering good quality care to patients and ensure that consultants are up to date and fit to practise. The directory produced by the British Association of Day Surgery (BADS) aims to promote Day Surgery by reducing inpatient stays, and improving outcomes. The supplement adds to the information available to providers in showing how performance has changed and improved in day surgery but also shows that there still exist wide variation between providers which both providers and commissioners can use to review and optimise local performance.
CHKS LIMITED	CHKS LIMITED	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	CHKS Limited aims to produce/analyse statistics using HES data to help the NHS perform its duties. Data provided are only used by CHKS for the purposes, activities, and outputs defined in this agreement. CHKS Limited uses HES data to support and indirectly improve the provision of patient care by healthcare organisations and supporting NHS functions in England, Scotland, Wales, and Northern Ireland. NHS organisations using CHKS services benchmark and compare themselves against both national and local peers dependant on the casemix and provision of activity therefore a national dataset is required to allow such benchmarks to take place. Typically an NHS organisation will select a range of comparative providers from the national dataset, however some NHS organisations also wish to benchmark against a national acute non-specialist provider peer. In addition CHKS services allow NHS organisations to interpret and analyse national indicators, such as HSMR and SHMI, which are available at a national level. CHKS has been providing similar services to NHS organisations for over 25 years. CHKS Limited’s use of the HES data is restricted to the following: 1. Benchmarking of services for NHS providers (NHS Trusts, Local Health Boards) and NHS commissioners (CCGs,CSUs, and Local Health Boards) where data are used for creation of indicators and peer groups and are made available through an online tool and in reports; 2. Market share analysis services for healthcare providers (NHS Trusts) and commissioners (CCGs, and CSUs); 3. Data analysis toolkit services for healthcare providers (NHS Trusts, Local Health Boards) and commissioners (CCGs, and CSUs); 4. Mortality profiling service for NHS Trusts to review mortality; 5. Consultant appraisal services for NHS Trusts; 6. CHKS national Top Hospital awards celebrating success for NHS Trusts; 7. ‘Learning from the Best’ case studies for NHS organisations; 8. Providing a one-off set of aggregated indicators for the British Association of Day Surgery (BADS) Directory of Procedures for NHS providers.	HES data provided are processed using proprietary data processing software which analyses, cleanses, groups, and outputs the data into service-based patient level databases. Any HES data are held only in pseudonymised form and are never directly linked with other datasets which could allow re-identification of HES data. As well as HES, CHKS Limted process other datasets (directly submitted patient data, and publicly available datasets – PLACE, SHMI, RTT,Friends & Family Test, PROMS, Reference Costs, Staff Survey, Patient Survey, Cancer Waits, CDIFF/MRSA, Safety Thermometer, CQC Intelligence Monitoring). These other datasets are not directly linked to HES but are available as indicators. A user could view an indicator derived from HES data (e.g. Average LoS, Mortality) on the same screen as indicators derived from the other datasets mentioned. Using the patient level databases held CHKS Limited generates different sets of benchmarks, indicators, risk models, peer groups, and records which are then used for the services defined below: 1. Benchmarking service to NHS providers and NHS commissioners – HES data are processed into an aggregated comparative database used to provide indicator level benchmarks both online and in reports. 2. Market share analysis services – HES data are processed into an aggregated provider or commissioner based comparative market share databases. 3. Data Analysis toolkit service – HES data are processed into a pseudonymised patient database on which NHS providers and NHS commissioners can run summary peer reports. 4. Mortality profiling service – HES data processed and accessible at record level in pseudonymised form for client site only. 5. Consultant appraisal service – HES data processed and used to create aggregated consultant peer groups for comparative analysis. This is the only activity that utilises the Consultant Code field. 6. CHKS national Top Hospital awards – HES data are processed and used to create aggregated indicators held at Trust level. 7. NHS case studies – uses the aggregated comparative HES database also used for the benchmarking services; 8. BADS Directory of Procedures supplement - uses the aggregated comparative HES database also used for the benchmarking services. This has now been delivered and published as of October 2016. CHKS use 5 'core' years of full data, plus year to date to generate their outputs. An additional year is retained purely to allow Spells which ended in the earliest year to be generated (for example, spells which ended in 2011/12 but started in 2010/11, resulting in the retention of the 2010/11 purely for this purpose until it is superceded). This is to allow sufficient historic comparison of previous years performance. Once the 2016/17 annual refresh data have been received and processed CHKS will delete the 2010/11 HES data.	HES data will only be used in processed form in solely the following outputs: 1. CHKS live – this is a secure online portal which is accessible by authorised and authenticated users at contracted CHKS client sites and authorised and authenticated CHKS staff. Users access the data through a range of indicator dashboards and scorecards presented at aggregate level. The benchmarking, market share analysis, data analysis toolkit, mortality profiling services, and consultant appraisal services are all accessible through the portal. Each client organisation is only given access to the specific services for which they have contracted. All users accessing CHKS live are informed they are required to comply with the HES Analysis Guide; 2. Consultant appraisal reporting – electronic or hard copy reports provided to NHS Trusts providing analysis of consultant performance for appraisal. HES data used are summarised and non-identifiable and used in peer data only. Consultant benchmarks are reported independently and are not linked to individual sites. The service uses the pseudonymised consultant identifier to aggregates of Finish Consultant Episodes data, in order to show relative workload and performance indicators for consultants in peer hospitals. This is reported at anonymised and aggregated level with no patient level drill down. No other detail of consultant activity is reported. 3. Bespoke reporting – electronic or hard copy reports provided to NHS Trusts, or recognised NHS functions, providing analysis and commentary on trends in healthcare. The data will not be released outside the NHS. All small numbers are suppressed in reports in accordance with the HES Analysis Guide. 4. National awards – Trust-level aggregated indicators based on quality, improvement and best practice, and are used to determine top performing NHS organisations. Awards are held on annual basis in May. 5. Case studies – electronic or hard copy reports provided to NHS organisations. Data are provided at aggregate level only and all small numbers are suppressed. 6. BADS Directory of Procedures – National Dataset to publish alongside the guide/directory produced and published by BADS which includes the target for procedures agreed by BADS. The National dataset supplement includes data that reflects outcomes for England, with planned management intent for day surgery, and is divided into cohorts showing the percentage of procedures successfully carried out on a day case basis. Included for each procedure are aggregated indicators reporting on the performance of the top 5%, 25% and 50% of hospitals with each operation. All data is aggregated to national level and published with all small numbers suppressed. This has now been delivered and published as of October 2016. Additional Information on the above outputs • CHKS live services containing HES data are used to provide indicator and peer level comparisons in aggregated form • Within the benchmarking service NHS providers can access pseudonymised and non-sensitive record-level data for their own activity to allow providers to review benchmarks at a granular level, however all peer comparisons are at aggregated and summarised level. NHS commissioners can only view aggregated and summarised indicator level benchmarks and cannot drill down to record-level data. • The Mortality profiling service allows NHS providers to access HES data for their specific activity where data is available at record level for the purposes of audits and review to allow NHS trusts to review mortality case and monitor and improve patient care. This data are not patient identifiable and is not linked to any client submitted data but provides information on diagnosis codes to allow meaningful audit of key conditions. • The Data Analysis Toolkit only allows NHS providers and NHS commissioners to see HES data aggregated in peer based reports. Users within the Data Analysis Toolkit create a tabulation by selecting from a range of available fields – the source data is at record level and the Data Analysis Toolkit then aggregates the data based on the fields the user selects. The user is then presented with the aggregated report and they do not see the record level data used to generate the tabulation. Peer based reports do not include Patient ID or Consultant ID fields. Users can download peer based reports. All users of DAT are required to accept a condition requiring adherence to the HES Analysis Guide before being permitted to run or download a Peer based report. • NHS organisations accessing these services do not have access to the HES Local Patient Identifier or the HES Consultant Identifiers through CHKS services. • Electronic or hard copy reports are provided to NHS Trusts providing analysis of consultant performance for appraisal. Consultant Code will be used in Consultant Appraisal reporting to allow consultant appraisal reports to contain activity carried out by the consultant at other NHS Trusts. This is currently not possible using pseudonymised consultant code. • The appraisal reports are made available directly to the named consultant in each trust or to the appraisal manager/Coordinator/revalidation responsible officer or medical director in the Trust where the consultant’s main contract is held. consultant’s work can be seen in other trusts but in summarised and aggregated form and not at patient level – the consultant report summarises activity, length of stay, day cases rates, complications, readmissions, and mortality indicators. • Consultant reports will not be made available to the public by CHKS and will solely be provided to NHS Trusts that are clients of CHKS. • The clear consultant code field will only be used for the Consultant Appraisal objective, processing and outputs. Other relevant supporting information: No individuals, doctors, consultants, or patients are ever identified in CHKS products, systems, or reporting using data provided by NHS Digital. HES data are held in the above outputs only in pseudonymised form and are never associated with other datasets held in CHKS systems. Record level data are never made available to any third party organisation unless specifically stated. Whilst CHKS Limited is part of the Capita Group only aggregated data are only used by CHKS Limited for the purposes above and not shared with other organisations within the Capita Group. CHKS displays a HES data statement wherever HES data are used. The statement says: “HES data re-used with the permission of The Health and Social Care Information Centre. All rights reserved.” This statement is present on all CHKS live pages, any extracts downloaded from CHKS live, and all bespoke consultancy reports. The CHKS live secure online system is held on CHKS Limited servers. The servers are physically stored in a Six Degrees Group datacentre which is located in England. Processed record-level HES data is loaded to these servers by CHKS. Six Degrees Group do not have access to any of the outputs or data; they provide physical locations to host the servers and network infrastructure but the servers are exclusively managed and used by CHKS Limited.	CHKS is currently contracted to provide the above described to around 80 NHS organisations within England, Scotland, Wales, and Northern Ireland with contracts extending into 2018 with the primary purpose to improve patient care within the NHS. CHKS contract renewals rates within the last year are approximately 75%. The service provided by CHKS provides assurance for trust boards and demonstrates NHS organisational commitment to continuous improvement. The services support internal analysis of performance, provides evidence for targeting improvement, demonstrate trends over time and progress made in priority areas, compare trust performance against local targets and national peers, and engage users across client organisations. NHS organisations are using CHKS services to: • Improve the quality of care; • Increase efficiency; • Increase productivity; • Monitor and reduce mortality; • Improve patient safety; • Reduce length of stay; • Reduce costs by analysing admissions; • Reduce readmissions; • Improve data quality; • Monitor, analyse, and understand commissioning; • Understand service users, populations, and providers; • Plan services; • Manage risks; • Improve utilisation; • Respond to regulatory requirements. Realisation of these benefits is ongoing however to support the usage of NHS Digital supplied data CHKS has made available case studies. These case studies include: • Royal Surrey County NHS Foundation Trust, who have used CHKS benchmarking tools and achieved improvements to patient safety. This was managed through the creation of indicators and benchmarks against length of stay, complications, misadventures, and mortality. Improvements to data quality were also realised. • Mid Cheshire Hospitals NHS Foundation Trust, who have used CHKS benchmarking tools and risk adjusted mortality models to identify areas where mortality indices were high and then take steps to improve the quality of care and reduce mortality. • North East London CSU, who have used CHKS benchmarking tools and national HES data to achieve improvements in provider productivity by using benchmarked data to set targets for acute Trust providers. Full case studies and more information can be found on the CHKS website at http://www.chks.co.uk/Knowledge-Base. In addition feedback from NHS organisations includes (those marked * were delivered in the second half of 2016): • a provider in the South West uses CHKS benchmarking tools where HES data is used to generate comparative metrics for Mortality where the provider delivers specialist care The output from these metrics feeds into a Quality Intelligence Group chaired by the Medical Director which identifies issues across the provider, feeds back to the appropriate departments, and monitors ongoing performance, therefore improving patient care; • a provider in the Midlands uses CHKS benchmarking tools where HES data is used as both a national peer and as a pre-defined peer of clinically similar organisations to review performance using a suite of indicator scorecards. Output from these scorecards is reviewed at board level and by review groups within the trust and fed back to clinicians to help improve patient care. • a large Trust in the Midlands receives a quarterly reporting pack derived from CHKS benchmarking tools covering a range of key indicators, including mortality, readmissions, length of stay and quality indicators (using national and quality account HES peers) which is used by the Trust to monitor improvements and highlight outliers with the clinical directorates. * CHKS Limited introduced a new commissioner based benchmarking and analysis tool in 2016, which is being used to support commissioning organisations in England. The tool allows commissioners to view benchmarked indicators across a range of key reporting areas. In addition a number of new population standardised indicators are available including Total Spells, Total OP Attendances, Total A&E Attendances, Admitted Bed Days, Readmissions, Unplanned Hospitalisation, Emergency admissions for acute conditions that should not usually require hospital admission, and Emergency admissions for children with Lower Respiratory Tract Infections (LRTIs) that should not usually require hospital admission. These new indicators provide observed and standardised expected values allowing commissioners to understand performance for their population. CHKS would anticipate reporting further benefits at a future renewal. CHKS Limited’s request for clear Consultant Code (consult) data item, for use in NHS consultant appraisal, will add further benefits as follows. Medical appraisal has been a requirement for consultants since 2001. Medical appraisal is used to support the delivery of a safe, committed, compassionate, and caring service to patients, help supervise and support doctors, and support the process of medical revalidation (Source: NHS England Medical Appraisal Policy). The addition of clear consultant code will allow CHKS Limited to provide better information to support consultant appraisal where consultants work for more than one NHS Trust. Currently those consultants who work across more than one trust are unable at present to see aggregated data in one report unless both trusts happen to be a client of CHKS. In addition many consultants now move throughout their consultant career and may often wish to have access to multiple site data which CHKS Limited have in HES but needs the Consultant Code to identify the consultant to provide trended aggregated information on performance case mix or workload. Allowing this will mean that NHS Trusts can see performance for new consultants at their first appraisal rather than relying on limited information from a few months’ work and so improving the appraisal process. Improvements to consultant appraisal will ultimately allow NHS Trusts to ensure their consultants are delivering good quality care to patients and ensure that consultants are up to date and fit to practise. The directory produced by the British Association of Day Surgery (BADS) aims to promote Day Surgery by reducing inpatient stays, and improving outcomes. The supplement adds to the information available to providers in showing how performance has changed and improved in day surgery but also shows that there still exist wide variation between providers which both providers and commissioners can use to review and optimise local performance.
CHKS LIMITED	CHKS LIMITED	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	CHKS Limited aims to produce/analyse statistics using HES data to help the NHS perform its duties. Data provided are only used by CHKS for the purposes, activities, and outputs defined in this agreement. CHKS Limited uses HES data to support and indirectly improve the provision of patient care by healthcare organisations and supporting NHS functions in England, Scotland, Wales, and Northern Ireland. NHS organisations using CHKS services benchmark and compare themselves against both national and local peers dependant on the casemix and provision of activity therefore a national dataset is required to allow such benchmarks to take place. Typically an NHS organisation will select a range of comparative providers from the national dataset, however some NHS organisations also wish to benchmark against a national acute non-specialist provider peer. In addition CHKS services allow NHS organisations to interpret and analyse national indicators, such as HSMR and SHMI, which are available at a national level. CHKS has been providing similar services to NHS organisations for over 25 years. CHKS Limited’s use of the HES data is restricted to the following: 1. Benchmarking of services for NHS providers (NHS Trusts, Local Health Boards) and NHS commissioners (CCGs,CSUs, and Local Health Boards) where data are used for creation of indicators and peer groups and are made available through an online tool and in reports; 2. Market share analysis services for healthcare providers (NHS Trusts) and commissioners (CCGs, and CSUs); 3. Data analysis toolkit services for healthcare providers (NHS Trusts, Local Health Boards) and commissioners (CCGs, and CSUs); 4. Mortality profiling service for NHS Trusts to review mortality; 5. Consultant appraisal services for NHS Trusts; 6. CHKS national Top Hospital awards celebrating success for NHS Trusts; 7. ‘Learning from the Best’ case studies for NHS organisations; 8. Providing a one-off set of aggregated indicators for the British Association of Day Surgery (BADS) Directory of Procedures for NHS providers.	HES data provided are processed using proprietary data processing software which analyses, cleanses, groups, and outputs the data into service-based patient level databases. Any HES data are held only in pseudonymised form and are never directly linked with other datasets which could allow re-identification of HES data. As well as HES, CHKS Limted process other datasets (directly submitted patient data, and publicly available datasets – PLACE, SHMI, RTT,Friends & Family Test, PROMS, Reference Costs, Staff Survey, Patient Survey, Cancer Waits, CDIFF/MRSA, Safety Thermometer, CQC Intelligence Monitoring). These other datasets are not directly linked to HES but are available as indicators. A user could view an indicator derived from HES data (e.g. Average LoS, Mortality) on the same screen as indicators derived from the other datasets mentioned. Using the patient level databases held CHKS Limited generates different sets of benchmarks, indicators, risk models, peer groups, and records which are then used for the services defined below: 1. Benchmarking service to NHS providers and NHS commissioners – HES data are processed into an aggregated comparative database used to provide indicator level benchmarks both online and in reports. 2. Market share analysis services – HES data are processed into an aggregated provider or commissioner based comparative market share databases. 3. Data Analysis toolkit service – HES data are processed into a pseudonymised patient database on which NHS providers and NHS commissioners can run summary peer reports. 4. Mortality profiling service – HES data processed and accessible at record level in pseudonymised form for client site only. 5. Consultant appraisal service – HES data processed and used to create aggregated consultant peer groups for comparative analysis. This is the only activity that utilises the Consultant Code field. 6. CHKS national Top Hospital awards – HES data are processed and used to create aggregated indicators held at Trust level. 7. NHS case studies – uses the aggregated comparative HES database also used for the benchmarking services; 8. BADS Directory of Procedures supplement - uses the aggregated comparative HES database also used for the benchmarking services. This has now been delivered and published as of October 2016. CHKS use 5 'core' years of full data, plus year to date to generate their outputs. An additional year is retained purely to allow Spells which ended in the earliest year to be generated (for example, spells which ended in 2011/12 but started in 2010/11, resulting in the retention of the 2010/11 purely for this purpose until it is superceded). This is to allow sufficient historic comparison of previous years performance. Once the 2016/17 annual refresh data have been received and processed CHKS will delete the 2010/11 HES data.	HES data will only be used in processed form in solely the following outputs: 1. CHKS live – this is a secure online portal which is accessible by authorised and authenticated users at contracted CHKS client sites and authorised and authenticated CHKS staff. Users access the data through a range of indicator dashboards and scorecards presented at aggregate level. The benchmarking, market share analysis, data analysis toolkit, mortality profiling services, and consultant appraisal services are all accessible through the portal. Each client organisation is only given access to the specific services for which they have contracted. All users accessing CHKS live are informed they are required to comply with the HES Analysis Guide; 2. Consultant appraisal reporting – electronic or hard copy reports provided to NHS Trusts providing analysis of consultant performance for appraisal. HES data used are summarised and non-identifiable and used in peer data only. Consultant benchmarks are reported independently and are not linked to individual sites. The service uses the pseudonymised consultant identifier to aggregates of Finish Consultant Episodes data, in order to show relative workload and performance indicators for consultants in peer hospitals. This is reported at anonymised and aggregated level with no patient level drill down. No other detail of consultant activity is reported. 3. Bespoke reporting – electronic or hard copy reports provided to NHS Trusts, or recognised NHS functions, providing analysis and commentary on trends in healthcare. The data will not be released outside the NHS. All small numbers are suppressed in reports in accordance with the HES Analysis Guide. 4. National awards – Trust-level aggregated indicators based on quality, improvement and best practice, and are used to determine top performing NHS organisations. Awards are held on annual basis in May. 5. Case studies – electronic or hard copy reports provided to NHS organisations. Data are provided at aggregate level only and all small numbers are suppressed. 6. BADS Directory of Procedures – National Dataset to publish alongside the guide/directory produced and published by BADS which includes the target for procedures agreed by BADS. The National dataset supplement includes data that reflects outcomes for England, with planned management intent for day surgery, and is divided into cohorts showing the percentage of procedures successfully carried out on a day case basis. Included for each procedure are aggregated indicators reporting on the performance of the top 5%, 25% and 50% of hospitals with each operation. All data is aggregated to national level and published with all small numbers suppressed. This has now been delivered and published as of October 2016. Additional Information on the above outputs • CHKS live services containing HES data are used to provide indicator and peer level comparisons in aggregated form • Within the benchmarking service NHS providers can access pseudonymised and non-sensitive record-level data for their own activity to allow providers to review benchmarks at a granular level, however all peer comparisons are at aggregated and summarised level. NHS commissioners can only view aggregated and summarised indicator level benchmarks and cannot drill down to record-level data. • The Mortality profiling service allows NHS providers to access HES data for their specific activity where data is available at record level for the purposes of audits and review to allow NHS trusts to review mortality case and monitor and improve patient care. This data are not patient identifiable and is not linked to any client submitted data but provides information on diagnosis codes to allow meaningful audit of key conditions. • The Data Analysis Toolkit only allows NHS providers and NHS commissioners to see HES data aggregated in peer based reports. Users within the Data Analysis Toolkit create a tabulation by selecting from a range of available fields – the source data is at record level and the Data Analysis Toolkit then aggregates the data based on the fields the user selects. The user is then presented with the aggregated report and they do not see the record level data used to generate the tabulation. Peer based reports do not include Patient ID or Consultant ID fields. Users can download peer based reports. All users of DAT are required to accept a condition requiring adherence to the HES Analysis Guide before being permitted to run or download a Peer based report. • NHS organisations accessing these services do not have access to the HES Local Patient Identifier or the HES Consultant Identifiers through CHKS services. • Electronic or hard copy reports are provided to NHS Trusts providing analysis of consultant performance for appraisal. Consultant Code will be used in Consultant Appraisal reporting to allow consultant appraisal reports to contain activity carried out by the consultant at other NHS Trusts. This is currently not possible using pseudonymised consultant code. • The appraisal reports are made available directly to the named consultant in each trust or to the appraisal manager/Coordinator/revalidation responsible officer or medical director in the Trust where the consultant’s main contract is held. consultant’s work can be seen in other trusts but in summarised and aggregated form and not at patient level – the consultant report summarises activity, length of stay, day cases rates, complications, readmissions, and mortality indicators. • Consultant reports will not be made available to the public by CHKS and will solely be provided to NHS Trusts that are clients of CHKS. • The clear consultant code field will only be used for the Consultant Appraisal objective, processing and outputs. Other relevant supporting information: No individuals, doctors, consultants, or patients are ever identified in CHKS products, systems, or reporting using data provided by NHS Digital. HES data are held in the above outputs only in pseudonymised form and are never associated with other datasets held in CHKS systems. Record level data are never made available to any third party organisation unless specifically stated. Whilst CHKS Limited is part of the Capita Group only aggregated data are only used by CHKS Limited for the purposes above and not shared with other organisations within the Capita Group. CHKS displays a HES data statement wherever HES data are used. The statement says: “HES data re-used with the permission of The Health and Social Care Information Centre. All rights reserved.” This statement is present on all CHKS live pages, any extracts downloaded from CHKS live, and all bespoke consultancy reports. The CHKS live secure online system is held on CHKS Limited servers. The servers are physically stored in a Six Degrees Group datacentre which is located in England. Processed record-level HES data is loaded to these servers by CHKS. Six Degrees Group do not have access to any of the outputs or data; they provide physical locations to host the servers and network infrastructure but the servers are exclusively managed and used by CHKS Limited.	CHKS is currently contracted to provide the above described to around 80 NHS organisations within England, Scotland, Wales, and Northern Ireland with contracts extending into 2018 with the primary purpose to improve patient care within the NHS. CHKS contract renewals rates within the last year are approximately 75%. The service provided by CHKS provides assurance for trust boards and demonstrates NHS organisational commitment to continuous improvement. The services support internal analysis of performance, provides evidence for targeting improvement, demonstrate trends over time and progress made in priority areas, compare trust performance against local targets and national peers, and engage users across client organisations. NHS organisations are using CHKS services to: • Improve the quality of care; • Increase efficiency; • Increase productivity; • Monitor and reduce mortality; • Improve patient safety; • Reduce length of stay; • Reduce costs by analysing admissions; • Reduce readmissions; • Improve data quality; • Monitor, analyse, and understand commissioning; • Understand service users, populations, and providers; • Plan services; • Manage risks; • Improve utilisation; • Respond to regulatory requirements. Realisation of these benefits is ongoing however to support the usage of NHS Digital supplied data CHKS has made available case studies. These case studies include: • Royal Surrey County NHS Foundation Trust, who have used CHKS benchmarking tools and achieved improvements to patient safety. This was managed through the creation of indicators and benchmarks against length of stay, complications, misadventures, and mortality. Improvements to data quality were also realised. • Mid Cheshire Hospitals NHS Foundation Trust, who have used CHKS benchmarking tools and risk adjusted mortality models to identify areas where mortality indices were high and then take steps to improve the quality of care and reduce mortality. • North East London CSU, who have used CHKS benchmarking tools and national HES data to achieve improvements in provider productivity by using benchmarked data to set targets for acute Trust providers. Full case studies and more information can be found on the CHKS website at http://www.chks.co.uk/Knowledge-Base. In addition feedback from NHS organisations includes (those marked * were delivered in the second half of 2016): • a provider in the South West uses CHKS benchmarking tools where HES data is used to generate comparative metrics for Mortality where the provider delivers specialist care The output from these metrics feeds into a Quality Intelligence Group chaired by the Medical Director which identifies issues across the provider, feeds back to the appropriate departments, and monitors ongoing performance, therefore improving patient care; • a provider in the Midlands uses CHKS benchmarking tools where HES data is used as both a national peer and as a pre-defined peer of clinically similar organisations to review performance using a suite of indicator scorecards. Output from these scorecards is reviewed at board level and by review groups within the trust and fed back to clinicians to help improve patient care. • a large Trust in the Midlands receives a quarterly reporting pack derived from CHKS benchmarking tools covering a range of key indicators, including mortality, readmissions, length of stay and quality indicators (using national and quality account HES peers) which is used by the Trust to monitor improvements and highlight outliers with the clinical directorates. * CHKS Limited introduced a new commissioner based benchmarking and analysis tool in 2016, which is being used to support commissioning organisations in England. The tool allows commissioners to view benchmarked indicators across a range of key reporting areas. In addition a number of new population standardised indicators are available including Total Spells, Total OP Attendances, Total A&E Attendances, Admitted Bed Days, Readmissions, Unplanned Hospitalisation, Emergency admissions for acute conditions that should not usually require hospital admission, and Emergency admissions for children with Lower Respiratory Tract Infections (LRTIs) that should not usually require hospital admission. These new indicators provide observed and standardised expected values allowing commissioners to understand performance for their population. CHKS would anticipate reporting further benefits at a future renewal. CHKS Limited’s request for clear Consultant Code (consult) data item, for use in NHS consultant appraisal, will add further benefits as follows. Medical appraisal has been a requirement for consultants since 2001. Medical appraisal is used to support the delivery of a safe, committed, compassionate, and caring service to patients, help supervise and support doctors, and support the process of medical revalidation (Source: NHS England Medical Appraisal Policy). The addition of clear consultant code will allow CHKS Limited to provide better information to support consultant appraisal where consultants work for more than one NHS Trust. Currently those consultants who work across more than one trust are unable at present to see aggregated data in one report unless both trusts happen to be a client of CHKS. In addition many consultants now move throughout their consultant career and may often wish to have access to multiple site data which CHKS Limited have in HES but needs the Consultant Code to identify the consultant to provide trended aggregated information on performance case mix or workload. Allowing this will mean that NHS Trusts can see performance for new consultants at their first appraisal rather than relying on limited information from a few months’ work and so improving the appraisal process. Improvements to consultant appraisal will ultimately allow NHS Trusts to ensure their consultants are delivering good quality care to patients and ensure that consultants are up to date and fit to practise. The directory produced by the British Association of Day Surgery (BADS) aims to promote Day Surgery by reducing inpatient stays, and improving outcomes. The supplement adds to the information available to providers in showing how performance has changed and improved in day surgery but also shows that there still exist wide variation between providers which both providers and commissioners can use to review and optimise local performance.
CHKS LIMITED	CHKS LIMITED	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	CHKS Limited aims to produce/analyse statistics using HES data to help the NHS perform its duties. Data provided are only used by CHKS for the purposes, activities, and outputs defined in this agreement. CHKS Limited uses HES data to support and indirectly improve the provision of patient care by healthcare organisations and supporting NHS functions in England, Scotland, Wales, and Northern Ireland. NHS organisations using CHKS services benchmark and compare themselves against both national and local peers dependant on the casemix and provision of activity therefore a national dataset is required to allow such benchmarks to take place. Typically an NHS organisation will select a range of comparative providers from the national dataset, however some NHS organisations also wish to benchmark against a national acute non-specialist provider peer. In addition CHKS services allow NHS organisations to interpret and analyse national indicators, such as HSMR and SHMI, which are available at a national level. CHKS has been providing similar services to NHS organisations for over 25 years. CHKS Limited’s use of the HES data is restricted to the following: 1. Benchmarking of services for NHS providers (NHS Trusts, Local Health Boards) and NHS commissioners (CCGs,CSUs, and Local Health Boards) where data are used for creation of indicators and peer groups and are made available through an online tool and in reports; 2. Market share analysis services for healthcare providers (NHS Trusts) and commissioners (CCGs, and CSUs); 3. Data analysis toolkit services for healthcare providers (NHS Trusts, Local Health Boards) and commissioners (CCGs, and CSUs); 4. Mortality profiling service for NHS Trusts to review mortality; 5. Consultant appraisal services for NHS Trusts; 6. CHKS national Top Hospital awards celebrating success for NHS Trusts; 7. ‘Learning from the Best’ case studies for NHS organisations; 8. Providing a one-off set of aggregated indicators for the British Association of Day Surgery (BADS) Directory of Procedures for NHS providers.	HES data provided are processed using proprietary data processing software which analyses, cleanses, groups, and outputs the data into service-based patient level databases. Any HES data are held only in pseudonymised form and are never directly linked with other datasets which could allow re-identification of HES data. As well as HES, CHKS Limted process other datasets (directly submitted patient data, and publicly available datasets – PLACE, SHMI, RTT,Friends & Family Test, PROMS, Reference Costs, Staff Survey, Patient Survey, Cancer Waits, CDIFF/MRSA, Safety Thermometer, CQC Intelligence Monitoring). These other datasets are not directly linked to HES but are available as indicators. A user could view an indicator derived from HES data (e.g. Average LoS, Mortality) on the same screen as indicators derived from the other datasets mentioned. Using the patient level databases held CHKS Limited generates different sets of benchmarks, indicators, risk models, peer groups, and records which are then used for the services defined below: 1. Benchmarking service to NHS providers and NHS commissioners – HES data are processed into an aggregated comparative database used to provide indicator level benchmarks both online and in reports. 2. Market share analysis services – HES data are processed into an aggregated provider or commissioner based comparative market share databases. 3. Data Analysis toolkit service – HES data are processed into a pseudonymised patient database on which NHS providers and NHS commissioners can run summary peer reports. 4. Mortality profiling service – HES data processed and accessible at record level in pseudonymised form for client site only. 5. Consultant appraisal service – HES data processed and used to create aggregated consultant peer groups for comparative analysis. This is the only activity that utilises the Consultant Code field. 6. CHKS national Top Hospital awards – HES data are processed and used to create aggregated indicators held at Trust level. 7. NHS case studies – uses the aggregated comparative HES database also used for the benchmarking services; 8. BADS Directory of Procedures supplement - uses the aggregated comparative HES database also used for the benchmarking services. This has now been delivered and published as of October 2016. CHKS use 5 'core' years of full data, plus year to date to generate their outputs. An additional year is retained purely to allow Spells which ended in the earliest year to be generated (for example, spells which ended in 2011/12 but started in 2010/11, resulting in the retention of the 2010/11 purely for this purpose until it is superceded). This is to allow sufficient historic comparison of previous years performance. Once the 2016/17 annual refresh data have been received and processed CHKS will delete the 2010/11 HES data.	HES data will only be used in processed form in solely the following outputs: 1. CHKS live – this is a secure online portal which is accessible by authorised and authenticated users at contracted CHKS client sites and authorised and authenticated CHKS staff. Users access the data through a range of indicator dashboards and scorecards presented at aggregate level. The benchmarking, market share analysis, data analysis toolkit, mortality profiling services, and consultant appraisal services are all accessible through the portal. Each client organisation is only given access to the specific services for which they have contracted. All users accessing CHKS live are informed they are required to comply with the HES Analysis Guide; 2. Consultant appraisal reporting – electronic or hard copy reports provided to NHS Trusts providing analysis of consultant performance for appraisal. HES data used are summarised and non-identifiable and used in peer data only. Consultant benchmarks are reported independently and are not linked to individual sites. The service uses the pseudonymised consultant identifier to aggregates of Finish Consultant Episodes data, in order to show relative workload and performance indicators for consultants in peer hospitals. This is reported at anonymised and aggregated level with no patient level drill down. No other detail of consultant activity is reported. 3. Bespoke reporting – electronic or hard copy reports provided to NHS Trusts, or recognised NHS functions, providing analysis and commentary on trends in healthcare. The data will not be released outside the NHS. All small numbers are suppressed in reports in accordance with the HES Analysis Guide. 4. National awards – Trust-level aggregated indicators based on quality, improvement and best practice, and are used to determine top performing NHS organisations. Awards are held on annual basis in May. 5. Case studies – electronic or hard copy reports provided to NHS organisations. Data are provided at aggregate level only and all small numbers are suppressed. 6. BADS Directory of Procedures – National Dataset to publish alongside the guide/directory produced and published by BADS which includes the target for procedures agreed by BADS. The National dataset supplement includes data that reflects outcomes for England, with planned management intent for day surgery, and is divided into cohorts showing the percentage of procedures successfully carried out on a day case basis. Included for each procedure are aggregated indicators reporting on the performance of the top 5%, 25% and 50% of hospitals with each operation. All data is aggregated to national level and published with all small numbers suppressed. This has now been delivered and published as of October 2016. Additional Information on the above outputs • CHKS live services containing HES data are used to provide indicator and peer level comparisons in aggregated form • Within the benchmarking service NHS providers can access pseudonymised and non-sensitive record-level data for their own activity to allow providers to review benchmarks at a granular level, however all peer comparisons are at aggregated and summarised level. NHS commissioners can only view aggregated and summarised indicator level benchmarks and cannot drill down to record-level data. • The Mortality profiling service allows NHS providers to access HES data for their specific activity where data is available at record level for the purposes of audits and review to allow NHS trusts to review mortality case and monitor and improve patient care. This data are not patient identifiable and is not linked to any client submitted data but provides information on diagnosis codes to allow meaningful audit of key conditions. • The Data Analysis Toolkit only allows NHS providers and NHS commissioners to see HES data aggregated in peer based reports. Users within the Data Analysis Toolkit create a tabulation by selecting from a range of available fields – the source data is at record level and the Data Analysis Toolkit then aggregates the data based on the fields the user selects. The user is then presented with the aggregated report and they do not see the record level data used to generate the tabulation. Peer based reports do not include Patient ID or Consultant ID fields. Users can download peer based reports. All users of DAT are required to accept a condition requiring adherence to the HES Analysis Guide before being permitted to run or download a Peer based report. • NHS organisations accessing these services do not have access to the HES Local Patient Identifier or the HES Consultant Identifiers through CHKS services. • Electronic or hard copy reports are provided to NHS Trusts providing analysis of consultant performance for appraisal. Consultant Code will be used in Consultant Appraisal reporting to allow consultant appraisal reports to contain activity carried out by the consultant at other NHS Trusts. This is currently not possible using pseudonymised consultant code. • The appraisal reports are made available directly to the named consultant in each trust or to the appraisal manager/Coordinator/revalidation responsible officer or medical director in the Trust where the consultant’s main contract is held. consultant’s work can be seen in other trusts but in summarised and aggregated form and not at patient level – the consultant report summarises activity, length of stay, day cases rates, complications, readmissions, and mortality indicators. • Consultant reports will not be made available to the public by CHKS and will solely be provided to NHS Trusts that are clients of CHKS. • The clear consultant code field will only be used for the Consultant Appraisal objective, processing and outputs. Other relevant supporting information: No individuals, doctors, consultants, or patients are ever identified in CHKS products, systems, or reporting using data provided by NHS Digital. HES data are held in the above outputs only in pseudonymised form and are never associated with other datasets held in CHKS systems. Record level data are never made available to any third party organisation unless specifically stated. Whilst CHKS Limited is part of the Capita Group only aggregated data are only used by CHKS Limited for the purposes above and not shared with other organisations within the Capita Group. CHKS displays a HES data statement wherever HES data are used. The statement says: “HES data re-used with the permission of The Health and Social Care Information Centre. All rights reserved.” This statement is present on all CHKS live pages, any extracts downloaded from CHKS live, and all bespoke consultancy reports. The CHKS live secure online system is held on CHKS Limited servers. The servers are physically stored in a Six Degrees Group datacentre which is located in England. Processed record-level HES data is loaded to these servers by CHKS. Six Degrees Group do not have access to any of the outputs or data; they provide physical locations to host the servers and network infrastructure but the servers are exclusively managed and used by CHKS Limited.	CHKS is currently contracted to provide the above described to around 80 NHS organisations within England, Scotland, Wales, and Northern Ireland with contracts extending into 2018 with the primary purpose to improve patient care within the NHS. CHKS contract renewals rates within the last year are approximately 75%. The service provided by CHKS provides assurance for trust boards and demonstrates NHS organisational commitment to continuous improvement. The services support internal analysis of performance, provides evidence for targeting improvement, demonstrate trends over time and progress made in priority areas, compare trust performance against local targets and national peers, and engage users across client organisations. NHS organisations are using CHKS services to: • Improve the quality of care; • Increase efficiency; • Increase productivity; • Monitor and reduce mortality; • Improve patient safety; • Reduce length of stay; • Reduce costs by analysing admissions; • Reduce readmissions; • Improve data quality; • Monitor, analyse, and understand commissioning; • Understand service users, populations, and providers; • Plan services; • Manage risks; • Improve utilisation; • Respond to regulatory requirements. Realisation of these benefits is ongoing however to support the usage of NHS Digital supplied data CHKS has made available case studies. These case studies include: • Royal Surrey County NHS Foundation Trust, who have used CHKS benchmarking tools and achieved improvements to patient safety. This was managed through the creation of indicators and benchmarks against length of stay, complications, misadventures, and mortality. Improvements to data quality were also realised. • Mid Cheshire Hospitals NHS Foundation Trust, who have used CHKS benchmarking tools and risk adjusted mortality models to identify areas where mortality indices were high and then take steps to improve the quality of care and reduce mortality. • North East London CSU, who have used CHKS benchmarking tools and national HES data to achieve improvements in provider productivity by using benchmarked data to set targets for acute Trust providers. Full case studies and more information can be found on the CHKS website at http://www.chks.co.uk/Knowledge-Base. In addition feedback from NHS organisations includes (those marked * were delivered in the second half of 2016): • a provider in the South West uses CHKS benchmarking tools where HES data is used to generate comparative metrics for Mortality where the provider delivers specialist care The output from these metrics feeds into a Quality Intelligence Group chaired by the Medical Director which identifies issues across the provider, feeds back to the appropriate departments, and monitors ongoing performance, therefore improving patient care; • a provider in the Midlands uses CHKS benchmarking tools where HES data is used as both a national peer and as a pre-defined peer of clinically similar organisations to review performance using a suite of indicator scorecards. Output from these scorecards is reviewed at board level and by review groups within the trust and fed back to clinicians to help improve patient care. • a large Trust in the Midlands receives a quarterly reporting pack derived from CHKS benchmarking tools covering a range of key indicators, including mortality, readmissions, length of stay and quality indicators (using national and quality account HES peers) which is used by the Trust to monitor improvements and highlight outliers with the clinical directorates. * CHKS Limited introduced a new commissioner based benchmarking and analysis tool in 2016, which is being used to support commissioning organisations in England. The tool allows commissioners to view benchmarked indicators across a range of key reporting areas. In addition a number of new population standardised indicators are available including Total Spells, Total OP Attendances, Total A&E Attendances, Admitted Bed Days, Readmissions, Unplanned Hospitalisation, Emergency admissions for acute conditions that should not usually require hospital admission, and Emergency admissions for children with Lower Respiratory Tract Infections (LRTIs) that should not usually require hospital admission. These new indicators provide observed and standardised expected values allowing commissioners to understand performance for their population. CHKS would anticipate reporting further benefits at a future renewal. CHKS Limited’s request for clear Consultant Code (consult) data item, for use in NHS consultant appraisal, will add further benefits as follows. Medical appraisal has been a requirement for consultants since 2001. Medical appraisal is used to support the delivery of a safe, committed, compassionate, and caring service to patients, help supervise and support doctors, and support the process of medical revalidation (Source: NHS England Medical Appraisal Policy). The addition of clear consultant code will allow CHKS Limited to provide better information to support consultant appraisal where consultants work for more than one NHS Trust. Currently those consultants who work across more than one trust are unable at present to see aggregated data in one report unless both trusts happen to be a client of CHKS. In addition many consultants now move throughout their consultant career and may often wish to have access to multiple site data which CHKS Limited have in HES but needs the Consultant Code to identify the consultant to provide trended aggregated information on performance case mix or workload. Allowing this will mean that NHS Trusts can see performance for new consultants at their first appraisal rather than relying on limited information from a few months’ work and so improving the appraisal process. Improvements to consultant appraisal will ultimately allow NHS Trusts to ensure their consultants are delivering good quality care to patients and ensure that consultants are up to date and fit to practise. The directory produced by the British Association of Day Surgery (BADS) aims to promote Day Surgery by reducing inpatient stays, and improving outcomes. The supplement adds to the information available to providers in showing how performance has changed and improved in day surgery but also shows that there still exist wide variation between providers which both providers and commissioners can use to review and optimise local performance.
CRAB CLINICAL INFORMATICS	CRAB CLINICAL INFORMATICS	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	CRAB Clinical Informatics Limited (C-Ci) is responsible for developing and marketing CRAB (Copeland s Risk Adjusted Barometer). Designed by the former National Director for Clinical Audit, CRAB is a web-based tool to evaluate quality and outcomes in a way which accurately reflects the clinical profile of patients treated. This is designed to provide a granular local dashboard to help NHS Trusts, and appropriate National Authorities (NHS England, CQC etc. – see below) to interpret mortality analysis (HSMR/SHMI) and understand safety in relation to avoidable harm, morbidity and areas for improvement. CRAB comprises two principal modules - Surgical and Medical. A third, complementary module focusing exclusively on obstetric care can also be applied if relevant. Reporting is designed to underpin the clinical governance strategy for individual and organisational practice. The focus is on local service line management and appraisal. No PID will be available or shared through CRAB Reviews. Trusts’ own data is available for their own internal reviews, investigations and appraisals, and not available to anyone outside the organisation other than C-Ci staff and agents (and national authorities upon request as indicated elsewhere in this application) under the terms of this agreement and strict ISA terms with the client Trust. For the avoidance of doubt, the reviews C-Ci will be producing using central HES will not be making patient level pseudonymised data available to anyone other than C-Ci staff for the purposes of compiling aggregate analysis. C-Ci’s central HES reviews are principally intended either for national bodies, or as initial baseline assessments for NHS Trusts which request them. Some NHS Trusts may wish to progress to a fully installed CRAB system. No patient level data provided under this agreement is provided to those Trusts. Those Trusts will enter into a separate agreement with CRAB for a flow of their own locally supplied SUS data, but that is outside this agreement, and the data provided under that local flow is not used in conjunction with any record level data under this agreement. Agents and national authorities : National authorities on behalf of which C-Ci may generate reports comprise specifically: CQC, Monitor, Department of Health/NHS England. C-Ci in addition has worked on a case-by case basis with the following commercial agents: PwC (initially as part of the Keogh Reviews and any follow up thereto), & Oliver Wyman, for specific quality improvement projects with the engagement of the client NHS Trusts. In all cases, neither the agents nor the authorities have access to the raw HES data, only the CRAB analysis. The CRAB modules - CRAB in Surgery generates case-mix adjusted outcomes using POSSUM (all variants) a respected audit methodology, recommended by the RCS. - CRAB Medical produces reports detailing trigger events which are indicative of iatrogenic harm. These can be grouped to understand overall quality of care, and potential deficiencies in process. Trigger events are triangulated from the published UK version of the Global Trigger Tool, a recognised approach for quality improvement in use widely as a manual sampling methodology across the NHS. - CRAB Maternity provides a composite dashboard, which risk-adjusts where appropriate for surgical obstetrics, and otherwise applies a validated group of “trigger”-style variables, drawn from academic work with the RCOG. The HES dataset is being obtained to: - assist in objective benchmarking of organisations, - expedite quality analysis on behalf of the CQC, notably quarterly national horizon scanning of NHS hospitals for high levels of morbidity as an early-warning and targeting tool for inspections - replay CRAB analysis and reporting to individual client NHS organisations. The analysis provided to the Keogh review team in 2013 was based on a bulk extract of HES & ONS data supplied by the HSCIC (now NHS Digital) for the 14 hospitals concerned. A similar exercise was also conducted using HES data on behalf of Monitor, and again as a research project investigating emergency laparotomy practice with Royal United Bath Hospital NHS Trust. C-Ci Ltd. therefore has an established record of receiving and handling data from the HSCIC/NHS Digital and successfully passed a compliance audit by NHS Digital in January 2017. With the extension of this system following the Keogh Review and the newly defined roles of the CQC, which has now commissioned ongoing quarterly analysis of all Trusts from C-Ci, as well as to make the analysis more widely available to Trusts individually, this application is to seek a renewal of the current licensed use of NHS Digital data. For individual organisations, the HES dataset enables C-Ci to highlight areas of care where standards require improvement, or vice versa where good care practices can be rewarded and lessons learnt from where good care exists. C-Ci aim to provide the most accurate account possible of the care being delivered by an organisation, so the organisation can see the whole picture and make decisions based on their current data performance. In order to produce an accurate and reliable quality review of the care an organisation is delivering at that point in time, C-Ci therefore require the most current available data to make a worthwhile analysis. The same argument also holds true for C-Ci’s work for the CQC, where the intention is to provide targeted, key lines of enquiry for on-site inspection teams, and therefore the more current the data, the more salient the lines of enquiry will be. C-Ci does work strategically with healthcare management consultants (currently PwC – permission from NHS Digital will be sought for any additions or changes), for the purposes of enabling Trusts strategically and operationally to make the most of the CRAB system. This is through offering a supported programme to implement the organisational changes necessary (both cultural and systemic) to achieve improvements in quality identified by CRAB analysis. This has been flagged in previous documentation. CRAB analysis in the form of static reports may therefore be made available to these named partner organisations in the context of initiating collaborative improvement projects with the NHS Trusts concerned, but under no circumstances would partner consultants be given access to the raw data, nor is the live CRAB database shared with them. Such analysis provided to partner consultants is in aggregate (trend lines/bulk period analyses) form only small numbers are suppressed and there is no reference to record-level data. In terms of customer base, presently approximately 90% are NHS providers/commissioners, and 10% are national bodies. This excludes the single commercial sector organisation using CRAB reports as outlined above.	In so far as C-Ci create interactive databases for those client NHS organisations to interrogate, this involves setting up bespoke CRAB databases for each organisation that purchases the software: these databases allow for the client to pull up standard CRAB reports, and also, to a certain degree, to make bespoke queries. However, in all cases, the database they interrogate contains only data relating to that NHS organisation (and it is already data that they have submitted centrally), and the access is only to CRAB analysis and reports, not to the raw data. Data is loaded onto the CRAB server hosted at L2S2 where individual database are created for individual NHS trust’s data only. Here they can be accessed over N3 by designated and approved C-Ci staff, or employees of the relevant NHS Trust. The HES data will not be linked to any other data other than publicly available data, or (as anonymised output) to other data relating to the Trust in fulfilment of the purpose outlined within this application. Data can only be viewed over the N3 network, and customers can only view the data and recall CRAB reports. It is not possible to download the raw data. No record level data will be stored outside L2S2. All outputs are aggregated with small number suppression in line with the HES Analysis Guide. C-Ci will hold a maximum of 3 years of finalised NHS Digital data at any time. Older data will be destroyed on a rolling basis when final data for a new year is received.	Research analysis with NHS partners: - Orthopaedic usage of HDU study in conjunction with Royal National Orthopaedic Hospital Stanmore and correlation with mortality to be completed, Autumn 2017 - Analysis of AKI reduction with specific nursing intervention, in conjunction with South Tees NHSFT, completed Summer 2017. Acute Kidney Injury (AKI) is one of the biggest causes of avoidable mortality and morbidity in the UK, with up to 100,000 deaths each year in hospital being associated with AKI. Up to 30% of AKI cases associated with death could be prevented with the right care. CRAB supported South Tees NHS Trust in identifying deteriorating trends in acute kidney injury (AKI) in years 2015/16, measure the improvement and cost benefit of early effective intervention in 2016/17. As a result CRAB was able to demonstrate to the Trust that it saved on average £4,700 per AKI episode avoided and over the period since intervention AKI has reduced by over 36%, which has resulted in a saving to the Trust of over £850,000 per year. Now this successful programme has shared the pathway and been consulted by other NHS trusts, who have been made aware of our AKI project through their links to CRAB clinical informatics (including Imperial, Frimley Park, Wexham Park, North Devon, St Helen’s, Lincoln, Yeovil, Bartholomew’s, The Royal London and Southend NHS Trusts). Furthermore, the South Tees AKI and CRAB Teams have been shortlisted for a national Patient Safety Award. Analysis of clinical outcomes, comprising: - clinically risk adjusted surgical mortality and morbidity for the case-mix of patients treated. - review of avoidable harm events across surgical, medical and nursing care - (where relevant) review of obstetric care The data detailed will be used to: - provide overview reports to NHS bodies (national and local); - create interactive databases for those bodies to interrogate the reporting interactively; and Potentially contribute to validation of future changes to CRAB algorithms (which are reviewed annually against a proprietary international dataset to accommodate any changes in clinical practice globally). Reviews using NHS Digital data are one-off for local organisations, prior to a decision to procure the full CRAB system (which uses locally supplied data as indicated elsewhere), and may be one-off or periodic for national organisations (e.g. snapshot reports for Keogh Review, Quarterly ongoing reports for CQC). Where an ongoing license with an organisation has been agreed, the above analysis will be available both from the live interactive database described above for the NHS Client’s use, and scheduled monthly reports will also be generated to track immediate trends by way of internal clinical governance and service management. Again, for clarity, the ongoing licensed version of CRAB uses locally supplied data and there is no crossover with NHS Digital data. Reviews and scheduled reports of acute care and performance of an organisation are for that organisation only, unless being reviewed for a national body such as the CQC or Monitor, with timescales determined by the executives.	Benefits of the monthly extract to date include successfully providing Rapid Reviews of hospital care for Sir Mike Richards , Chief Inspector of Hospitals as and when requested for supporting CQC Inspections, and since then, a national horizon-scanning process on an ongoing, quarterly basis designed to identify: - outlying organisations which may perform acceptably for mortality, but potentially have underlying problems in relation to morbidity which have hitherto gone unnoticed and may easily escalate, with an impact on avoidable mortality. This may include: o problems with failure to rescue deteriorating patients o recovering seriously ill patients who have deteriorated as a result of poor care, which has impacted upon cost and capacity to treat others - within the above, which specific areas of an organisation are most problematic, in order that any subsequent enquiry or inspection can be effectively targeted. - outlying/improving organisations at the good end of the spectrum, to highlight where the NHS is developing a learning culture, in line with Sir Mike Richard’s report in March 2017. CRAB has also supported hospital organisations when wanting to review certain procedures and their outcomes to monitor performance. This is typically done as a one-off exercise, again to help Trust Boards and clinical leaders target their improvement efforts in the right areas. The benefit of CRAB to clinicians, managers and Board members at hospital Trusts includes the ability to: - understand quality of care on an ongoing basis, and to set in place appropriate governance and monitoring systems. - receive early warning of problems and monitor trends for deterioration in practice quality - rapidly investigate and interpret SHMI results (and other mortality data such as HSMR), to understand root cause and underlying patterns in relation to mortality, in accordance with DH policy guidance that provider organisations should have their own localised clinical dashboards for this purpose. - monitor quality beyond basic mortality: assessing morbidity and avoidable harm, as a fundamental move towards continuous quality improvement - generate appraisal documentation for clinical staff - improve accuracy of coding - understand the clinical risks (or mortality and complications) for each individual patient and have frank and open discussions with them. These benefits can also be aggregated to a national level, in so far as the DH and regulatory bodies (Monitor and CQC) can use the information to drill into more detailed analysis of organisational performance where appropriate, particularly in relation to morbidity and avoidable harm - as is envisaged to support CQC inspection activity and quality baselining by Monitor. For example as C-Ci has been commissioned by to undertake a quarterly national review of NHS medicine and surgery outcomes of all NHS hospitals in England on behalf of the Care Quality Commission (CQC). In essence this will provide CQC inspectors with an early warning analysis at a Trust and Department level based on a more in depth view of medical and ward-based care alongside clinically risk-adjusted surgical practice than may otherwise be possible with standard mortality and statistical analysis. The focus in particular is on understanding morbidity as well as mortality, and areas where morbidity may be just as much the result of omission to treat/failure to rescue as it may be the result of active error. C-Ci provides the CQC inspectors with a list of the top 20 NHS trusts: a) with the highest rate (percentage) of patients with 4 or more triggers of avoidable harm in general medicine and general surgery; and b) with the highest mortality rate for patients experiencing 4 or more triggers. In addition, C-Ci provide the CQC with the 20 NHS trusts with the lowest rates of avoidable harm as exemplars of best and safest practice. In relation to the interactive databases, the reporting can be made available to individual patient level, subject to restricted permissions, enabling organisations to conduct detailed audits and investigations where necessary (e.g. critical incident & SUI reporting). However, in any given case, the raw data itself is not accessed, and in relation to the reports, these are only accessible to the organisation and staff concerned, being hosted in a dedicated, encrypted environment and subject to IG-compliant processes for log-in and individualised permissions. As indicated in relation to C-Ci current licence for historical HES, C-Ci s data hosting partners, L2S2 Ltd, are IG Toolkit Level 3, and ISO27001 certified. L2S2 are now also ISO13485 , ISO 9001 and CMDCAS Certified. Effective 27/05/2016. C-Ci is currently working towards ISO9001.
CRAB CLINICAL INFORMATICS	CRAB CLINICAL INFORMATICS	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	CRAB Clinical Informatics Limited (C-Ci) is responsible for developing and marketing CRAB (Copeland s Risk Adjusted Barometer). Designed by the former National Director for Clinical Audit, CRAB is a web-based tool to evaluate quality and outcomes in a way which accurately reflects the clinical profile of patients treated. This is designed to provide a granular local dashboard to help NHS Trusts, and appropriate National Authorities (NHS England, CQC etc. – see below) to interpret mortality analysis (HSMR/SHMI) and understand safety in relation to avoidable harm, morbidity and areas for improvement. CRAB comprises two principal modules - Surgical and Medical. A third, complementary module focusing exclusively on obstetric care can also be applied if relevant. Reporting is designed to underpin the clinical governance strategy for individual and organisational practice. The focus is on local service line management and appraisal. No PID will be available or shared through CRAB Reviews. Trusts’ own data is available for their own internal reviews, investigations and appraisals, and not available to anyone outside the organisation other than C-Ci staff and agents (and national authorities upon request as indicated elsewhere in this application) under the terms of this agreement and strict ISA terms with the client Trust. For the avoidance of doubt, the reviews C-Ci will be producing using central HES will not be making patient level pseudonymised data available to anyone other than C-Ci staff for the purposes of compiling aggregate analysis. C-Ci’s central HES reviews are principally intended either for national bodies, or as initial baseline assessments for NHS Trusts which request them. Some NHS Trusts may wish to progress to a fully installed CRAB system. No patient level data provided under this agreement is provided to those Trusts. Those Trusts will enter into a separate agreement with CRAB for a flow of their own locally supplied SUS data, but that is outside this agreement, and the data provided under that local flow is not used in conjunction with any record level data under this agreement. Agents and national authorities : National authorities on behalf of which C-Ci may generate reports comprise specifically: CQC, Monitor, Department of Health/NHS England. C-Ci in addition has worked on a case-by case basis with the following commercial agents: PwC (initially as part of the Keogh Reviews and any follow up thereto), & Oliver Wyman, for specific quality improvement projects with the engagement of the client NHS Trusts. In all cases, neither the agents nor the authorities have access to the raw HES data, only the CRAB analysis. The CRAB modules - CRAB in Surgery generates case-mix adjusted outcomes using POSSUM (all variants) a respected audit methodology, recommended by the RCS. - CRAB Medical produces reports detailing trigger events which are indicative of iatrogenic harm. These can be grouped to understand overall quality of care, and potential deficiencies in process. Trigger events are triangulated from the published UK version of the Global Trigger Tool, a recognised approach for quality improvement in use widely as a manual sampling methodology across the NHS. - CRAB Maternity provides a composite dashboard, which risk-adjusts where appropriate for surgical obstetrics, and otherwise applies a validated group of “trigger”-style variables, drawn from academic work with the RCOG. The HES dataset is being obtained to: - assist in objective benchmarking of organisations, - expedite quality analysis on behalf of the CQC, notably quarterly national horizon scanning of NHS hospitals for high levels of morbidity as an early-warning and targeting tool for inspections - replay CRAB analysis and reporting to individual client NHS organisations. The analysis provided to the Keogh review team in 2013 was based on a bulk extract of HES & ONS data supplied by the HSCIC (now NHS Digital) for the 14 hospitals concerned. A similar exercise was also conducted using HES data on behalf of Monitor, and again as a research project investigating emergency laparotomy practice with Royal United Bath Hospital NHS Trust. C-Ci Ltd. therefore has an established record of receiving and handling data from the HSCIC/NHS Digital and successfully passed a compliance audit by NHS Digital in January 2017. With the extension of this system following the Keogh Review and the newly defined roles of the CQC, which has now commissioned ongoing quarterly analysis of all Trusts from C-Ci, as well as to make the analysis more widely available to Trusts individually, this application is to seek a renewal of the current licensed use of NHS Digital data. For individual organisations, the HES dataset enables C-Ci to highlight areas of care where standards require improvement, or vice versa where good care practices can be rewarded and lessons learnt from where good care exists. C-Ci aim to provide the most accurate account possible of the care being delivered by an organisation, so the organisation can see the whole picture and make decisions based on their current data performance. In order to produce an accurate and reliable quality review of the care an organisation is delivering at that point in time, C-Ci therefore require the most current available data to make a worthwhile analysis. The same argument also holds true for C-Ci’s work for the CQC, where the intention is to provide targeted, key lines of enquiry for on-site inspection teams, and therefore the more current the data, the more salient the lines of enquiry will be. C-Ci does work strategically with healthcare management consultants (currently PwC – permission from NHS Digital will be sought for any additions or changes), for the purposes of enabling Trusts strategically and operationally to make the most of the CRAB system. This is through offering a supported programme to implement the organisational changes necessary (both cultural and systemic) to achieve improvements in quality identified by CRAB analysis. This has been flagged in previous documentation. CRAB analysis in the form of static reports may therefore be made available to these named partner organisations in the context of initiating collaborative improvement projects with the NHS Trusts concerned, but under no circumstances would partner consultants be given access to the raw data, nor is the live CRAB database shared with them. Such analysis provided to partner consultants is in aggregate (trend lines/bulk period analyses) form only small numbers are suppressed and there is no reference to record-level data. In terms of customer base, presently approximately 90% are NHS providers/commissioners, and 10% are national bodies. This excludes the single commercial sector organisation using CRAB reports as outlined above.	In so far as C-Ci create interactive databases for those client NHS organisations to interrogate, this involves setting up bespoke CRAB databases for each organisation that purchases the software: these databases allow for the client to pull up standard CRAB reports, and also, to a certain degree, to make bespoke queries. However, in all cases, the database they interrogate contains only data relating to that NHS organisation (and it is already data that they have submitted centrally), and the access is only to CRAB analysis and reports, not to the raw data. Data is loaded onto the CRAB server hosted at L2S2 where individual database are created for individual NHS trust’s data only. Here they can be accessed over N3 by designated and approved C-Ci staff, or employees of the relevant NHS Trust. The HES data will not be linked to any other data other than publicly available data, or (as anonymised output) to other data relating to the Trust in fulfilment of the purpose outlined within this application. Data can only be viewed over the N3 network, and customers can only view the data and recall CRAB reports. It is not possible to download the raw data. No record level data will be stored outside L2S2. All outputs are aggregated with small number suppression in line with the HES Analysis Guide. C-Ci will hold a maximum of 3 years of finalised NHS Digital data at any time. Older data will be destroyed on a rolling basis when final data for a new year is received.	Research analysis with NHS partners: - Orthopaedic usage of HDU study in conjunction with Royal National Orthopaedic Hospital Stanmore and correlation with mortality to be completed, Autumn 2017 - Analysis of AKI reduction with specific nursing intervention, in conjunction with South Tees NHSFT, completed Summer 2017. Acute Kidney Injury (AKI) is one of the biggest causes of avoidable mortality and morbidity in the UK, with up to 100,000 deaths each year in hospital being associated with AKI. Up to 30% of AKI cases associated with death could be prevented with the right care. CRAB supported South Tees NHS Trust in identifying deteriorating trends in acute kidney injury (AKI) in years 2015/16, measure the improvement and cost benefit of early effective intervention in 2016/17. As a result CRAB was able to demonstrate to the Trust that it saved on average £4,700 per AKI episode avoided and over the period since intervention AKI has reduced by over 36%, which has resulted in a saving to the Trust of over £850,000 per year. Now this successful programme has shared the pathway and been consulted by other NHS trusts, who have been made aware of our AKI project through their links to CRAB clinical informatics (including Imperial, Frimley Park, Wexham Park, North Devon, St Helen’s, Lincoln, Yeovil, Bartholomew’s, The Royal London and Southend NHS Trusts). Furthermore, the South Tees AKI and CRAB Teams have been shortlisted for a national Patient Safety Award. Analysis of clinical outcomes, comprising: - clinically risk adjusted surgical mortality and morbidity for the case-mix of patients treated. - review of avoidable harm events across surgical, medical and nursing care - (where relevant) review of obstetric care The data detailed will be used to: - provide overview reports to NHS bodies (national and local); - create interactive databases for those bodies to interrogate the reporting interactively; and Potentially contribute to validation of future changes to CRAB algorithms (which are reviewed annually against a proprietary international dataset to accommodate any changes in clinical practice globally). Reviews using NHS Digital data are one-off for local organisations, prior to a decision to procure the full CRAB system (which uses locally supplied data as indicated elsewhere), and may be one-off or periodic for national organisations (e.g. snapshot reports for Keogh Review, Quarterly ongoing reports for CQC). Where an ongoing license with an organisation has been agreed, the above analysis will be available both from the live interactive database described above for the NHS Client’s use, and scheduled monthly reports will also be generated to track immediate trends by way of internal clinical governance and service management. Again, for clarity, the ongoing licensed version of CRAB uses locally supplied data and there is no crossover with NHS Digital data. Reviews and scheduled reports of acute care and performance of an organisation are for that organisation only, unless being reviewed for a national body such as the CQC or Monitor, with timescales determined by the executives.	Benefits of the monthly extract to date include successfully providing Rapid Reviews of hospital care for Sir Mike Richards , Chief Inspector of Hospitals as and when requested for supporting CQC Inspections, and since then, a national horizon-scanning process on an ongoing, quarterly basis designed to identify: - outlying organisations which may perform acceptably for mortality, but potentially have underlying problems in relation to morbidity which have hitherto gone unnoticed and may easily escalate, with an impact on avoidable mortality. This may include: o problems with failure to rescue deteriorating patients o recovering seriously ill patients who have deteriorated as a result of poor care, which has impacted upon cost and capacity to treat others - within the above, which specific areas of an organisation are most problematic, in order that any subsequent enquiry or inspection can be effectively targeted. - outlying/improving organisations at the good end of the spectrum, to highlight where the NHS is developing a learning culture, in line with Sir Mike Richard’s report in March 2017. CRAB has also supported hospital organisations when wanting to review certain procedures and their outcomes to monitor performance. This is typically done as a one-off exercise, again to help Trust Boards and clinical leaders target their improvement efforts in the right areas. The benefit of CRAB to clinicians, managers and Board members at hospital Trusts includes the ability to: - understand quality of care on an ongoing basis, and to set in place appropriate governance and monitoring systems. - receive early warning of problems and monitor trends for deterioration in practice quality - rapidly investigate and interpret SHMI results (and other mortality data such as HSMR), to understand root cause and underlying patterns in relation to mortality, in accordance with DH policy guidance that provider organisations should have their own localised clinical dashboards for this purpose. - monitor quality beyond basic mortality: assessing morbidity and avoidable harm, as a fundamental move towards continuous quality improvement - generate appraisal documentation for clinical staff - improve accuracy of coding - understand the clinical risks (or mortality and complications) for each individual patient and have frank and open discussions with them. These benefits can also be aggregated to a national level, in so far as the DH and regulatory bodies (Monitor and CQC) can use the information to drill into more detailed analysis of organisational performance where appropriate, particularly in relation to morbidity and avoidable harm - as is envisaged to support CQC inspection activity and quality baselining by Monitor. For example as C-Ci has been commissioned by to undertake a quarterly national review of NHS medicine and surgery outcomes of all NHS hospitals in England on behalf of the Care Quality Commission (CQC). In essence this will provide CQC inspectors with an early warning analysis at a Trust and Department level based on a more in depth view of medical and ward-based care alongside clinically risk-adjusted surgical practice than may otherwise be possible with standard mortality and statistical analysis. The focus in particular is on understanding morbidity as well as mortality, and areas where morbidity may be just as much the result of omission to treat/failure to rescue as it may be the result of active error. C-Ci provides the CQC inspectors with a list of the top 20 NHS trusts: a) with the highest rate (percentage) of patients with 4 or more triggers of avoidable harm in general medicine and general surgery; and b) with the highest mortality rate for patients experiencing 4 or more triggers. In addition, C-Ci provide the CQC with the 20 NHS trusts with the lowest rates of avoidable harm as exemplars of best and safest practice. In relation to the interactive databases, the reporting can be made available to individual patient level, subject to restricted permissions, enabling organisations to conduct detailed audits and investigations where necessary (e.g. critical incident & SUI reporting). However, in any given case, the raw data itself is not accessed, and in relation to the reports, these are only accessible to the organisation and staff concerned, being hosted in a dedicated, encrypted environment and subject to IG-compliant processes for log-in and individualised permissions. As indicated in relation to C-Ci current licence for historical HES, C-Ci s data hosting partners, L2S2 Ltd, are IG Toolkit Level 3, and ISO27001 certified. L2S2 are now also ISO13485 , ISO 9001 and CMDCAS Certified. Effective 27/05/2016. C-Ci is currently working towards ISO9001.
CRAB CLINICAL INFORMATICS	CRAB CLINICAL INFORMATICS	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	CRAB Clinical Informatics Limited (C-Ci) is responsible for developing and marketing CRAB (Copeland s Risk Adjusted Barometer). Designed by the former National Director for Clinical Audit, CRAB is a web-based tool to evaluate quality and outcomes in a way which accurately reflects the clinical profile of patients treated. This is designed to provide a granular local dashboard to help NHS Trusts, and appropriate National Authorities (NHS England, CQC etc. – see below) to interpret mortality analysis (HSMR/SHMI) and understand safety in relation to avoidable harm, morbidity and areas for improvement. CRAB comprises two principal modules - Surgical and Medical. A third, complementary module focusing exclusively on obstetric care can also be applied if relevant. Reporting is designed to underpin the clinical governance strategy for individual and organisational practice. The focus is on local service line management and appraisal. No PID will be available or shared through CRAB Reviews. Trusts’ own data is available for their own internal reviews, investigations and appraisals, and not available to anyone outside the organisation other than C-Ci staff and agents (and national authorities upon request as indicated elsewhere in this application) under the terms of this agreement and strict ISA terms with the client Trust. For the avoidance of doubt, the reviews C-Ci will be producing using central HES will not be making patient level pseudonymised data available to anyone other than C-Ci staff for the purposes of compiling aggregate analysis. C-Ci’s central HES reviews are principally intended either for national bodies, or as initial baseline assessments for NHS Trusts which request them. Some NHS Trusts may wish to progress to a fully installed CRAB system. No patient level data provided under this agreement is provided to those Trusts. Those Trusts will enter into a separate agreement with CRAB for a flow of their own locally supplied SUS data, but that is outside this agreement, and the data provided under that local flow is not used in conjunction with any record level data under this agreement. Agents and national authorities : National authorities on behalf of which C-Ci may generate reports comprise specifically: CQC, Monitor, Department of Health/NHS England. C-Ci in addition has worked on a case-by case basis with the following commercial agents: PwC (initially as part of the Keogh Reviews and any follow up thereto), & Oliver Wyman, for specific quality improvement projects with the engagement of the client NHS Trusts. In all cases, neither the agents nor the authorities have access to the raw HES data, only the CRAB analysis. The CRAB modules - CRAB in Surgery generates case-mix adjusted outcomes using POSSUM (all variants) a respected audit methodology, recommended by the RCS. - CRAB Medical produces reports detailing trigger events which are indicative of iatrogenic harm. These can be grouped to understand overall quality of care, and potential deficiencies in process. Trigger events are triangulated from the published UK version of the Global Trigger Tool, a recognised approach for quality improvement in use widely as a manual sampling methodology across the NHS. - CRAB Maternity provides a composite dashboard, which risk-adjusts where appropriate for surgical obstetrics, and otherwise applies a validated group of “trigger”-style variables, drawn from academic work with the RCOG. The HES dataset is being obtained to: - assist in objective benchmarking of organisations, - expedite quality analysis on behalf of the CQC, notably quarterly national horizon scanning of NHS hospitals for high levels of morbidity as an early-warning and targeting tool for inspections - replay CRAB analysis and reporting to individual client NHS organisations. The analysis provided to the Keogh review team in 2013 was based on a bulk extract of HES & ONS data supplied by the HSCIC (now NHS Digital) for the 14 hospitals concerned. A similar exercise was also conducted using HES data on behalf of Monitor, and again as a research project investigating emergency laparotomy practice with Royal United Bath Hospital NHS Trust. C-Ci Ltd. therefore has an established record of receiving and handling data from the HSCIC/NHS Digital and successfully passed a compliance audit by NHS Digital in January 2017. With the extension of this system following the Keogh Review and the newly defined roles of the CQC, which has now commissioned ongoing quarterly analysis of all Trusts from C-Ci, as well as to make the analysis more widely available to Trusts individually, this application is to seek a renewal of the current licensed use of NHS Digital data. For individual organisations, the HES dataset enables C-Ci to highlight areas of care where standards require improvement, or vice versa where good care practices can be rewarded and lessons learnt from where good care exists. C-Ci aim to provide the most accurate account possible of the care being delivered by an organisation, so the organisation can see the whole picture and make decisions based on their current data performance. In order to produce an accurate and reliable quality review of the care an organisation is delivering at that point in time, C-Ci therefore require the most current available data to make a worthwhile analysis. In addition, analysis is at the consultant level by GMC code (CONSULT field) in order to verify speciality of clinical practice within a NHS Trust, when working strategically with individual Trusts. The same argument also holds true for C-Ci’s work for the CQC, where the intention is to provide targeted, key lines of enquiry for on-site inspection teams, and therefore the more current the data, the more salient the lines of enquiry will be. C-Ci does work strategically with healthcare management consultants (currently PwC – permission from NHS Digital will be sought for any additions or changes), for the purposes of enabling Trusts strategically and operationally to make the most of the CRAB system. This is through offering a supported programme to implement the organisational changes necessary (both cultural and systemic) to achieve improvements in quality identified by CRAB analysis. This has been flagged in previous documentation. CRAB analysis in the form of static reports may therefore be made available to these named partner organisations in the context of initiating collaborative improvement projects with the NHS Trusts concerned, but under no circumstances would partner consultants be given access to the raw data, nor is the live CRAB database shared with them. Such analysis provided to partner consultants is in aggregate (trend lines/bulk period analyses) form only small numbers are suppressed and there is no reference to record-level data. In terms of customer base, presently approximately 90% are NHS providers/commissioners, and 10% are national bodies. This excludes the single commercial sector organisation using CRAB reports as outlined above.	In so far as C-Ci create interactive databases for those client NHS organisations to interrogate, this involves setting up bespoke CRAB databases for each organisation that purchases the software: these databases allow for the client to pull up standard CRAB reports, and also, to a certain degree, to make bespoke queries. However, in all cases, the database they interrogate contains only data relating to that NHS organisation (and it is already data that they have submitted centrally), and the access is only to CRAB analysis and reports, not to the raw data. Data is loaded onto the CRAB server hosted at L2S2 where individual database are created for individual NHS trust’s data only. Here they can be accessed over N3 by designated and approved C-Ci staff, or employees of the relevant NHS Trust. The HES data will not be linked to any other data other than publicly available data, or (as anonymised output) to other data relating to the Trust in fulfilment of the purpose outlined within this application. Data can only be viewed over the N3 network, and customers can only view the data and recall CRAB reports. It is not possible to download the raw data. No record level data will be stored outside L2S2. All outputs are aggregated with small number suppression in line with the HES Analysis Guide. Consultant level data is aggregated and batched into quarterly or yearly data so as not to compromise patient identifiable data. C-Ci will hold a maximum of 3 years of finalised NHS Digital data at any time. Older data will be destroyed on a rolling basis when final data for a new year is received.	Research analysis with NHS partners: - Orthopaedic usage of HDU study in conjunction with Royal National Orthopaedic Hospital Stanmore and correlation with mortality to be completed, Autumn 2017 - Analysis of AKI reduction with specific nursing intervention, in conjunction with South Tees NHSFT, completed Summer 2017. Acute Kidney Injury (AKI) is one of the biggest causes of avoidable mortality and morbidity in the UK, with up to 100,000 deaths each year in hospital being associated with AKI. Up to 30% of AKI cases associated with death could be prevented with the right care. CRAB supported South Tees NHS Trust in identifying deteriorating trends in acute kidney injury (AKI) in years 2015/16, measure the improvement and cost benefit of early effective intervention in 2016/17. As a result CRAB was able to demonstrate to the Trust that it saved on average £4,700 per AKI episode avoided and over the period since intervention AKI has reduced by over 36%, which has resulted in a saving to the Trust of over £850,000 per year. Now this successful programme has shared the pathway and been consulted by other NHS trusts, who have been made aware of our AKI project through their links to CRAB clinical informatics (including Imperial, Frimley Park, Wexham Park, North Devon, St Helen’s, Lincoln, Yeovil, Bartholomew’s, The Royal London and Southend NHS Trusts). Furthermore, the South Tees AKI and CRAB Teams have been shortlisted for a national Patient Safety Award. Analysis of clinical outcomes, comprising: - clinically risk adjusted surgical mortality and morbidity for the case-mix of patients treated. - review of avoidable harm events across surgical, medical and nursing care - (where relevant) review of obstetric care The data detailed will be used to: - provide overview reports to NHS bodies (national and local); - create interactive databases for those bodies to interrogate the reporting interactively; and Potentially contribute to validation of future changes to CRAB algorithms (which are reviewed annually against a proprietary international dataset to accommodate any changes in clinical practice globally). Reviews using NHS Digital data are one-off for local organisations, prior to a decision to procure the full CRAB system (which uses locally supplied data as indicated elsewhere), and may be one-off or periodic for national organisations (e.g. snapshot reports for Keogh Review, Quarterly ongoing reports for CQC). Where an ongoing license with an organisation has been agreed, the above analysis will be available both from the live interactive database described above for the NHS Client’s use, and scheduled monthly reports will also be generated to track immediate trends by way of internal clinical governance and service management. Again, for clarity, the ongoing licensed version of CRAB uses locally supplied data and there is no crossover with NHS Digital data. Reviews and scheduled reports of acute care and performance of an organisation are for that organisation only, unless being reviewed for a national body such as the CQC or Monitor, with timescales determined by the executives.	Benefits of the monthly extract to date include successfully providing Rapid Reviews of hospital care for Sir Mike Richards , Chief Inspector of Hospitals as and when requested for supporting CQC Inspections, and since then, a national horizon-scanning process on an ongoing, quarterly basis designed to identify: - outlying organisations which may perform acceptably for mortality, but potentially have underlying problems in relation to morbidity which have hitherto gone unnoticed and may easily escalate, with an impact on avoidable mortality. This may include: o problems with failure to rescue deteriorating patients o recovering seriously ill patients who have deteriorated as a result of poor care, which has impacted upon cost and capacity to treat others - within the above, which specific areas of an organisation are most problematic, in order that any subsequent enquiry or inspection can be effectively targeted. - outlying/improving organisations at the good end of the spectrum, to highlight where the NHS is developing a learning culture, in line with Sir Mike Richard’s report in March 2017. CRAB has also supported hospital organisations when wanting to review certain procedures and their outcomes to monitor performance. This is typically done as a one-off exercise, again to help Trust Boards and clinical leaders target their improvement efforts in the right areas. The benefit of CRAB to clinicians, managers and Board members at hospital Trusts includes the ability to: - understand quality of care on an ongoing basis, and to set in place appropriate governance and monitoring systems. - receive early warning of problems and monitor trends for deterioration in practice quality - rapidly investigate and interpret SHMI results (and other mortality data such as HSMR), to understand root cause and underlying patterns in relation to mortality, in accordance with DH policy guidance that provider organisations should have their own localised clinical dashboards for this purpose. - monitor quality beyond basic mortality: assessing morbidity and avoidable harm, as a fundamental move towards continuous quality improvement - generate appraisal documentation for clinical staff - improve accuracy of coding - understand the clinical risks (or mortality and complications) for each individual patient and have frank and open discussions with them. These benefits can also be aggregated to a national level, in so far as the DH and regulatory bodies (Monitor and CQC) can use the information to drill into more detailed analysis of organisational performance where appropriate, particularly in relation to morbidity and avoidable harm - as is envisaged to support CQC inspection activity and quality baselining by Monitor. For example as C-Ci has been commissioned by to undertake a quarterly national review of NHS medicine and surgery outcomes of all NHS hospitals in England on behalf of the Care Quality Commission (CQC). In essence this will provide CQC inspectors with an early warning analysis at a Trust and Department level based on a more in depth view of medical and ward-based care alongside clinically risk-adjusted surgical practice than may otherwise be possible with standard mortality and statistical analysis. The focus in particular is on understanding morbidity as well as mortality, and areas where morbidity may be just as much the result of omission to treat/failure to rescue as it may be the result of active error. C-Ci provides the CQC inspectors with a list of the top 20 NHS trusts: a) with the highest rate (percentage) of patients with 4 or more triggers of avoidable harm in general medicine and general surgery; and b) with the highest mortality rate for patients experiencing 4 or more triggers. In addition, C-Ci provide the CQC with the 20 NHS trusts with the lowest rates of avoidable harm as exemplars of best and safest practice. In relation to the interactive databases, the reporting can be made available to individual patient level, subject to restricted permissions, enabling organisations to conduct detailed audits and investigations where necessary (e.g. critical incident & SUI reporting). However, in any given case, the raw data itself is not accessed, and in relation to the reports, these are only accessible to the organisation and staff concerned, being hosted in a dedicated, encrypted environment and subject to IG-compliant processes for log-in and individualised permissions. As indicated in relation to C-Ci current licence for historical HES, C-Ci s data hosting partners, L2S2 Ltd, are IG Toolkit Level 3, and ISO27001 certified. L2S2 are now also ISO13485 , ISO 9001 and CMDCAS Certified. Effective 27/05/2016. C-Ci is currently working towards ISO9001.
CRAB CLINICAL INFORMATICS	CRAB CLINICAL INFORMATICS	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	CRAB Clinical Informatics Limited (C-Ci) is responsible for developing and marketing CRAB (Copeland s Risk Adjusted Barometer). Designed by the former National Director for Clinical Audit, CRAB is a web-based tool to evaluate quality and outcomes in a way which accurately reflects the clinical profile of patients treated. This is designed to provide a granular local dashboard to help NHS Trusts, and appropriate National Authorities (NHS England, CQC etc. – see below) to interpret mortality analysis (HSMR/SHMI) and understand safety in relation to avoidable harm, morbidity and areas for improvement. CRAB comprises two principal modules - Surgical and Medical. A third, complementary module focusing exclusively on obstetric care can also be applied if relevant. Reporting is designed to underpin the clinical governance strategy for individual and organisational practice. The focus is on local service line management and appraisal. No PID will be available or shared through CRAB Reviews. Trusts’ own data is available for their own internal reviews, investigations and appraisals, and not available to anyone outside the organisation other than C-Ci staff and agents (and national authorities upon request as indicated elsewhere in this application) under the terms of this agreement and strict ISA terms with the client Trust. For the avoidance of doubt, the reviews C-Ci will be producing using central HES will not be making patient level pseudonymised data available to anyone other than C-Ci staff for the purposes of compiling aggregate analysis. C-Ci’s central HES reviews are principally intended either for national bodies, or as initial baseline assessments for NHS Trusts which request them. Some NHS Trusts may wish to progress to a fully installed CRAB system. No patient level data provided under this agreement is provided to those Trusts. Those Trusts will enter into a separate agreement with CRAB for a flow of their own locally supplied SUS data, but that is outside this agreement, and the data provided under that local flow is not used in conjunction with any record level data under this agreement. Agents and national authorities : National authorities on behalf of which C-Ci may generate reports comprise specifically: CQC, Monitor, Department of Health/NHS England. C-Ci in addition has worked on a case-by case basis with the following commercial agents: PwC (initially as part of the Keogh Reviews and any follow up thereto), & Oliver Wyman, for specific quality improvement projects with the engagement of the client NHS Trusts. In all cases, neither the agents nor the authorities have access to the raw HES data, only the CRAB analysis. The CRAB modules - CRAB in Surgery generates case-mix adjusted outcomes using POSSUM (all variants) a respected audit methodology, recommended by the RCS. - CRAB Medical produces reports detailing trigger events which are indicative of iatrogenic harm. These can be grouped to understand overall quality of care, and potential deficiencies in process. Trigger events are triangulated from the published UK version of the Global Trigger Tool, a recognised approach for quality improvement in use widely as a manual sampling methodology across the NHS. - CRAB Maternity provides a composite dashboard, which risk-adjusts where appropriate for surgical obstetrics, and otherwise applies a validated group of “trigger”-style variables, drawn from academic work with the RCOG. The HES dataset is being obtained to: - assist in objective benchmarking of organisations, - expedite quality analysis on behalf of the CQC, notably quarterly national horizon scanning of NHS hospitals for high levels of morbidity as an early-warning and targeting tool for inspections - replay CRAB analysis and reporting to individual client NHS organisations. The analysis provided to the Keogh review team in 2013 was based on a bulk extract of HES & ONS data supplied by the HSCIC (now NHS Digital) for the 14 hospitals concerned. A similar exercise was also conducted using HES data on behalf of Monitor, and again as a research project investigating emergency laparotomy practice with Royal United Bath Hospital NHS Trust. C-Ci Ltd. therefore has an established record of receiving and handling data from the HSCIC/NHS Digital and successfully passed a compliance audit by NHS Digital in January 2017. With the extension of this system following the Keogh Review and the newly defined roles of the CQC, which has now commissioned ongoing quarterly analysis of all Trusts from C-Ci, as well as to make the analysis more widely available to Trusts individually, this application is to seek a renewal of the current licensed use of NHS Digital data. For individual organisations, the HES dataset enables C-Ci to highlight areas of care where standards require improvement, or vice versa where good care practices can be rewarded and lessons learnt from where good care exists. C-Ci aim to provide the most accurate account possible of the care being delivered by an organisation, so the organisation can see the whole picture and make decisions based on their current data performance. In order to produce an accurate and reliable quality review of the care an organisation is delivering at that point in time, C-Ci therefore require the most current available data to make a worthwhile analysis. In addition, analysis is at the consultant level by GMC code (CONSULT field) in order to verify speciality of clinical practice within a NHS Trust, when working strategically with individual Trusts. The same argument also holds true for C-Ci’s work for the CQC, where the intention is to provide targeted, key lines of enquiry for on-site inspection teams, and therefore the more current the data, the more salient the lines of enquiry will be. C-Ci does work strategically with healthcare management consultants (currently PwC – permission from NHS Digital will be sought for any additions or changes), for the purposes of enabling Trusts strategically and operationally to make the most of the CRAB system. This is through offering a supported programme to implement the organisational changes necessary (both cultural and systemic) to achieve improvements in quality identified by CRAB analysis. This has been flagged in previous documentation. CRAB analysis in the form of static reports may therefore be made available to these named partner organisations in the context of initiating collaborative improvement projects with the NHS Trusts concerned, but under no circumstances would partner consultants be given access to the raw data, nor is the live CRAB database shared with them. Such analysis provided to partner consultants is in aggregate (trend lines/bulk period analyses) form only small numbers are suppressed and there is no reference to record-level data. In terms of customer base, presently approximately 90% are NHS providers/commissioners, and 10% are national bodies. This excludes the single commercial sector organisation using CRAB reports as outlined above.	In so far as C-Ci create interactive databases for those client NHS organisations to interrogate, this involves setting up bespoke CRAB databases for each organisation that purchases the software: these databases allow for the client to pull up standard CRAB reports, and also, to a certain degree, to make bespoke queries. However, in all cases, the database they interrogate contains only data relating to that NHS organisation (and it is already data that they have submitted centrally), and the access is only to CRAB analysis and reports, not to the raw data. Data is loaded onto the CRAB server hosted at L2S2 where individual database are created for individual NHS trust’s data only. Here they can be accessed over N3 by designated and approved C-Ci staff, or employees of the relevant NHS Trust. The HES data will not be linked to any other data other than publicly available data, or (as anonymised output) to other data relating to the Trust in fulfilment of the purpose outlined within this application. Data can only be viewed over the N3 network, and customers can only view the data and recall CRAB reports. It is not possible to download the raw data. No record level data will be stored outside L2S2. All outputs are aggregated with small number suppression in line with the HES Analysis Guide. Consultant level data is aggregated and batched into quarterly or yearly data so as not to compromise patient identifiable data. C-Ci will hold a maximum of 3 years of finalised NHS Digital data at any time. Older data will be destroyed on a rolling basis when final data for a new year is received.	Research analysis with NHS partners: - Orthopaedic usage of HDU study in conjunction with Royal National Orthopaedic Hospital Stanmore and correlation with mortality to be completed, Autumn 2017 - Analysis of AKI reduction with specific nursing intervention, in conjunction with South Tees NHSFT, completed Summer 2017. Acute Kidney Injury (AKI) is one of the biggest causes of avoidable mortality and morbidity in the UK, with up to 100,000 deaths each year in hospital being associated with AKI. Up to 30% of AKI cases associated with death could be prevented with the right care. CRAB supported South Tees NHS Trust in identifying deteriorating trends in acute kidney injury (AKI) in years 2015/16, measure the improvement and cost benefit of early effective intervention in 2016/17. As a result CRAB was able to demonstrate to the Trust that it saved on average £4,700 per AKI episode avoided and over the period since intervention AKI has reduced by over 36%, which has resulted in a saving to the Trust of over £850,000 per year. Now this successful programme has shared the pathway and been consulted by other NHS trusts, who have been made aware of our AKI project through their links to CRAB clinical informatics (including Imperial, Frimley Park, Wexham Park, North Devon, St Helen’s, Lincoln, Yeovil, Bartholomew’s, The Royal London and Southend NHS Trusts). Furthermore, the South Tees AKI and CRAB Teams have been shortlisted for a national Patient Safety Award. Analysis of clinical outcomes, comprising: - clinically risk adjusted surgical mortality and morbidity for the case-mix of patients treated. - review of avoidable harm events across surgical, medical and nursing care - (where relevant) review of obstetric care The data detailed will be used to: - provide overview reports to NHS bodies (national and local); - create interactive databases for those bodies to interrogate the reporting interactively; and Potentially contribute to validation of future changes to CRAB algorithms (which are reviewed annually against a proprietary international dataset to accommodate any changes in clinical practice globally). Reviews using NHS Digital data are one-off for local organisations, prior to a decision to procure the full CRAB system (which uses locally supplied data as indicated elsewhere), and may be one-off or periodic for national organisations (e.g. snapshot reports for Keogh Review, Quarterly ongoing reports for CQC). Where an ongoing license with an organisation has been agreed, the above analysis will be available both from the live interactive database described above for the NHS Client’s use, and scheduled monthly reports will also be generated to track immediate trends by way of internal clinical governance and service management. Again, for clarity, the ongoing licensed version of CRAB uses locally supplied data and there is no crossover with NHS Digital data. Reviews and scheduled reports of acute care and performance of an organisation are for that organisation only, unless being reviewed for a national body such as the CQC or Monitor, with timescales determined by the executives.	Benefits of the monthly extract to date include successfully providing Rapid Reviews of hospital care for Sir Mike Richards , Chief Inspector of Hospitals as and when requested for supporting CQC Inspections, and since then, a national horizon-scanning process on an ongoing, quarterly basis designed to identify: - outlying organisations which may perform acceptably for mortality, but potentially have underlying problems in relation to morbidity which have hitherto gone unnoticed and may easily escalate, with an impact on avoidable mortality. This may include: o problems with failure to rescue deteriorating patients o recovering seriously ill patients who have deteriorated as a result of poor care, which has impacted upon cost and capacity to treat others - within the above, which specific areas of an organisation are most problematic, in order that any subsequent enquiry or inspection can be effectively targeted. - outlying/improving organisations at the good end of the spectrum, to highlight where the NHS is developing a learning culture, in line with Sir Mike Richard’s report in March 2017. CRAB has also supported hospital organisations when wanting to review certain procedures and their outcomes to monitor performance. This is typically done as a one-off exercise, again to help Trust Boards and clinical leaders target their improvement efforts in the right areas. The benefit of CRAB to clinicians, managers and Board members at hospital Trusts includes the ability to: - understand quality of care on an ongoing basis, and to set in place appropriate governance and monitoring systems. - receive early warning of problems and monitor trends for deterioration in practice quality - rapidly investigate and interpret SHMI results (and other mortality data such as HSMR), to understand root cause and underlying patterns in relation to mortality, in accordance with DH policy guidance that provider organisations should have their own localised clinical dashboards for this purpose. - monitor quality beyond basic mortality: assessing morbidity and avoidable harm, as a fundamental move towards continuous quality improvement - generate appraisal documentation for clinical staff - improve accuracy of coding - understand the clinical risks (or mortality and complications) for each individual patient and have frank and open discussions with them. These benefits can also be aggregated to a national level, in so far as the DH and regulatory bodies (Monitor and CQC) can use the information to drill into more detailed analysis of organisational performance where appropriate, particularly in relation to morbidity and avoidable harm - as is envisaged to support CQC inspection activity and quality baselining by Monitor. For example as C-Ci has been commissioned by to undertake a quarterly national review of NHS medicine and surgery outcomes of all NHS hospitals in England on behalf of the Care Quality Commission (CQC). In essence this will provide CQC inspectors with an early warning analysis at a Trust and Department level based on a more in depth view of medical and ward-based care alongside clinically risk-adjusted surgical practice than may otherwise be possible with standard mortality and statistical analysis. The focus in particular is on understanding morbidity as well as mortality, and areas where morbidity may be just as much the result of omission to treat/failure to rescue as it may be the result of active error. C-Ci provides the CQC inspectors with a list of the top 20 NHS trusts: a) with the highest rate (percentage) of patients with 4 or more triggers of avoidable harm in general medicine and general surgery; and b) with the highest mortality rate for patients experiencing 4 or more triggers. In addition, C-Ci provide the CQC with the 20 NHS trusts with the lowest rates of avoidable harm as exemplars of best and safest practice. In relation to the interactive databases, the reporting can be made available to individual patient level, subject to restricted permissions, enabling organisations to conduct detailed audits and investigations where necessary (e.g. critical incident & SUI reporting). However, in any given case, the raw data itself is not accessed, and in relation to the reports, these are only accessible to the organisation and staff concerned, being hosted in a dedicated, encrypted environment and subject to IG-compliant processes for log-in and individualised permissions. As indicated in relation to C-Ci current licence for historical HES, C-Ci s data hosting partners, L2S2 Ltd, are IG Toolkit Level 3, and ISO27001 certified. L2S2 are now also ISO13485 , ISO 9001 and CMDCAS Certified. Effective 27/05/2016. C-Ci is currently working towards ISO9001.
DEPARTMENT OF HEALTH (DH)	DEPARTMENT OF HEALTH (DH)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HDIS system enables organisations to access HES data for a wide range of data analytical purposes. The system is an online analytical processing tool through which the users of this organisation data has access to a wide range of analytical, graphical, statistical and reporting functions. Access is provided to the entire HES dataset (non-identifiable) for the specific purposes as listed below. The Department of Health will use the HDIS system through the analysis of HES data for the following purposes: - Benchmarking; - Provision of support services; - Producing publications including contributing to national and regional publications such as A7E reports; - Supporting the Government in the development and monitoring of policy; - Early analysis for projects and programmes to support commissioning and policy decisions; - Commissioning decisions; - Responding to and answering of parliamentary questions in a timely fashion as part of statutory duties. The analysis conducted by DH is wide ranging and will most often be used for internal DH purposes. DH analysts do however also provide support to other agencies including NHS England, PHE, NHS Blood and Transplant etc. Department of Health analytics team is sometimes under great pressure from No. 10 / Secretary of State (SofS) to provide statistics such as: • How many more operations (FCEs with a procedure or intervention) the NHS or individual providers are doing now compared to earlier years • Waiting times for common procedures such as hips, knees, cataracts in England compared to another devolved administration, usually Wales (with PEDW being the Welsh equivalent of HES) The analytics team carry out a project for the OECD to provide information on volumes and costs of specific procedures and groups of patients. The criteria used to determine which individual cases should or should not be included is fairly rigid and HES allows the criteria to be set to meet the requirements exactly. The data the team provide is used to create indicators of efficiency and productivity which are comparable on an international basis and are used for the “Health at a Glance” publication. A Recent example of HDIS use has been used to explore the determinants of emergency admissions from A&E from 2010 onwards. The research question was: are non-elective admissions from A&E driven only by demand-side factors (type and severity of condition)? Do supply-side factors (hospital capacity) matter? The team conducted this analysis as part of the value maps project: a piece of analysis HM Treasury commissioned from every central government Department in order to assess their understanding of current and potential efficiency and effectiveness. In terms of methodology, a logit model was used where the unit of observation was an A&E episode from 2010 onwards and the binary dependent variable described whether the episode ended up in a non-elective admission or not. This research project is currently on hold due to other emerging priorities however it is scheduled to be finalised after DH2020. The above project is an important example for the following reasons: (1) it was fundamental to have patient level data (as it was the only way to control for observable demand-side factors); (2) it was part of a high-profile piece of work (commissioned by (Director General of public spending and finance at HMT) and (Chief Economic Adviser at HMT), and presented to a panel of senior officials from prestigious organisations (Deputy National Statistician and Director General for Population and Public Policy at ONS) and (Chief Executive of the Behavioural Insights Team and Board Director)] Two further examples of how data are being used: a. Analysis of acute care data including bed days and emergency admissions to support the New Models of Care and Transformation programmes (both SofS priorities). Department Of Health rely on HES data to analyse time trends and local variation to feed into SoS Transformation meetings and other needs. b. Analysis of referrals to Outpatients – this has informed a range of policy work including extending the ability of AHPs to refer directly to Outpatient clinics, the savings potentially achievable by key interventions such as GP One Stop, local patterns of referral by demographics. Accident and Emergency is one of several compartments in the Model Hospital (MH). It has been developed by combining key indicators recommended by the Royal College of Emergency Medicine (RCEM) with productivity metrics recommended by Lord Carter operational productivity team. One of the purpose of the MH is to serve as a platform to enable Trusts to compare resource and associated clinical output, level of responsiveness as well as their overall financial productivity to that of their peers. Some of the indicators created using the data you provided are below: - % waiting <6 hours: RCEM opinion is that four other flow metrics in combination with the four hours standard waiting time performance metric are essential to optimizing the productivity of the emergency department. The ‘A&E 6hrs waiting time performance’ is one of the four metrics. - Aggregated Patient Delay (APD): This adds granularity to the 4hrs target and removes the false dichotomy in which 3 hrs. 59 minutes is regarded as a success and 4 hrs. 1 minute a failure. - Inpatient Daily Discharge Ratio (DDR): This enables hospitals to predict capacity shortfalls and allows the wider healthcare system to intervene to ameliorate such situations. Low ratios are known to be associated with increased A&E waits the next day. - Using HES to assess length of stay for elective and non-elective patients by day of the week to form a key benefit in the 7DS in hospital impact assessment – this is key analysis would have been impossible without HES. This will feed into the Impact Assessment on 7 Day Services. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	This application is for online access to the record level HES database via the HDIS2 system. The system is hosted and audited by NHS Digital meaning that large transfers of data to on-site servers is reduced and NHS Digital has the ability to audit the use and access to the data. HDIS is accessed via a two-factor secure authentication method to approved users who are in receipt of an encryption token ID. Users have to attend training before the account is set up and users are only permitted to access the datasets that are agreed within this agreement. Users log onto the HDIS system and are presented with a SAS software application called Enterprise Guide which presents the users with a list of available data sets and available reference data tables so that they can return appropriate descriptions to the coded data. The access and use of the system is fully auditable and all users have to comply with the use of the data as specified in this agreement. The software tool also provides users with the ability to perform full data minimisation and filtering of the HES data as part of processing activities. Users are not permitted to upload data into the system. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users will have access to record level or aggregate data containing small numbers downloaded from the HDIS system. All HDIS users with access to the HDIS system are substantive employees of DH. Following completion of the analysis the record level data will be securely destroyed. DH currently has 24 licenses for access to HDIS and have the option to apply for further licenses if required. Approval for additional licences will be managed by the NHS Digital.	Due to the nature of the organisation, outputs are often unknown in advance and these will be driven by changing policy and ministerial priorities. Any outputs that are produced from the system that are to be published or shared with a third party (individuals or organisations outside of the analytical team) will be aggregated with small number suppressed in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. Below are some recent examples of the uses of HES data within DH: • Input to the quality assurance of denominator data derived from KH03 (quarterly bed availability and occupancy) used by PHE in annual publication of Healthcare Associated Infections (HCAI) rates. • Development of Alcohol Attributable fractions. It is anticipated that a similar approach might be used in future for new developing public health analyses. Through analysis of the data it is possible to calculate the cost of alcohol to the NHS which are carried out annually to support DH policy teams business case. A similar approach has been taken for smoking. • Research into areas of current policy interest, eg pneumonia. • As part of the New Models of Care and Transformation agendas (both SoS priorities), a key efficiency metric that will be used to measure success is bed days. DH has utilised HES data to understand this metric further, i.e. what variables in HES are used to calculate bed days, how good is the measure, etc. DH are currently using the data to explore some possible hypotheses such as: - Whether there are more bed days for patients admitted in the week vs. at the weekend; and - Under what treatment specialties are bed days very high, etc. None of this work so far has been used for official briefings or publications, but it is very likely that HES will be needed in the near future for briefings and QA. DH intend to utilise the HES data for other metrics for new models of care (NMC) and Transformation, for example A&E attendances and performance against the A&E 4-hour waiting standard. • DH works closely with DfE on policy for hospital schools. A new model of funding for hospital schools is being developed and HES data is playing an important role in this. • OECD research into Purchasing Power Parity in healthcare provision – An analysis is being carried out on the activity and prices for delivery of certain specific healthcare services (inpatient and day case basis). To do this access to HES data is required which details this at HRG level. • Cross sectional and time series analysis to understand efficiency and productivity of healthcare providers – This analysis is to be used for work relating to the Lord Carter report on efficiency, reporting on measures of efficiency and productivity for Secretary of State and HMT • Ministerial briefings - On-going work to understanding the link between activity/workload and staffing levels, work to understand impact upon safety and quality of care. • Internal analysis to provide management information required for the spending review.	The use of HDIS allows DH analysts to have a secure access to a remotely hosted software application for the analysis of HES data. Having access to record level downloads will permit the following activities which are not possible/practical within the HDIS system itself: - following individual patient pathways through each of the datasets - following individual patient pathways chronologically - permits linkage of HES data to anonymous data (e.g. Health Resource Group tariff information) The provision of this tool enables rapid analysis to be performed on the most recent version of the data. The availability of this function is crucial to DH in circumstances where speedy analysis is required to react to either local public health, commissioning or research requirements. Access to the data helps to Inform national policy development aimed at the improvement of patient outcomes generally.
DEPARTMENT OF HEALTH (DH)	DEPARTMENT OF HEALTH (DH)	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HDIS system enables organisations to access HES data for a wide range of data analytical purposes. The system is an online analytical processing tool through which the users of this organisation data has access to a wide range of analytical, graphical, statistical and reporting functions. Access is provided to the entire HES dataset (non-identifiable) for the specific purposes as listed below. The Department of Health will use the HDIS system through the analysis of HES data for the following purposes: - Benchmarking; - Provision of support services; - Producing publications including contributing to national and regional publications such as A7E reports; - Supporting the Government in the development and monitoring of policy; - Early analysis for projects and programmes to support commissioning and policy decisions; - Commissioning decisions; - Responding to and answering of parliamentary questions in a timely fashion as part of statutory duties. The analysis conducted by DH is wide ranging and will most often be used for internal DH purposes. DH analysts do however also provide support to other agencies including NHS England, PHE, NHS Blood and Transplant etc. Department of Health analytics team is sometimes under great pressure from No. 10 / Secretary of State (SofS) to provide statistics such as: • How many more operations (FCEs with a procedure or intervention) the NHS or individual providers are doing now compared to earlier years • Waiting times for common procedures such as hips, knees, cataracts in England compared to another devolved administration, usually Wales (with PEDW being the Welsh equivalent of HES) The analytics team carry out a project for the OECD to provide information on volumes and costs of specific procedures and groups of patients. The criteria used to determine which individual cases should or should not be included is fairly rigid and HES allows the criteria to be set to meet the requirements exactly. The data the team provide is used to create indicators of efficiency and productivity which are comparable on an international basis and are used for the “Health at a Glance” publication. A Recent example of HDIS use has been used to explore the determinants of emergency admissions from A&E from 2010 onwards. The research question was: are non-elective admissions from A&E driven only by demand-side factors (type and severity of condition)? Do supply-side factors (hospital capacity) matter? The team conducted this analysis as part of the value maps project: a piece of analysis HM Treasury commissioned from every central government Department in order to assess their understanding of current and potential efficiency and effectiveness. In terms of methodology, a logit model was used where the unit of observation was an A&E episode from 2010 onwards and the binary dependent variable described whether the episode ended up in a non-elective admission or not. This research project is currently on hold due to other emerging priorities however it is scheduled to be finalised after DH2020. The above project is an important example for the following reasons: (1) it was fundamental to have patient level data (as it was the only way to control for observable demand-side factors); (2) it was part of a high-profile piece of work (commissioned by (Director General of public spending and finance at HMT) and (Chief Economic Adviser at HMT), and presented to a panel of senior officials from prestigious organisations (Deputy National Statistician and Director General for Population and Public Policy at ONS) and (Chief Executive of the Behavioural Insights Team and Board Director)] Two further examples of how data are being used: a. Analysis of acute care data including bed days and emergency admissions to support the New Models of Care and Transformation programmes (both SofS priorities). Department Of Health rely on HES data to analyse time trends and local variation to feed into SoS Transformation meetings and other needs. b. Analysis of referrals to Outpatients – this has informed a range of policy work including extending the ability of AHPs to refer directly to Outpatient clinics, the savings potentially achievable by key interventions such as GP One Stop, local patterns of referral by demographics. Accident and Emergency is one of several compartments in the Model Hospital (MH). It has been developed by combining key indicators recommended by the Royal College of Emergency Medicine (RCEM) with productivity metrics recommended by Lord Carter operational productivity team. One of the purpose of the MH is to serve as a platform to enable Trusts to compare resource and associated clinical output, level of responsiveness as well as their overall financial productivity to that of their peers. Some of the indicators created using the data you provided are below: - % waiting <6 hours: RCEM opinion is that four other flow metrics in combination with the four hours standard waiting time performance metric are essential to optimizing the productivity of the emergency department. The ‘A&E 6hrs waiting time performance’ is one of the four metrics. - Aggregated Patient Delay (APD): This adds granularity to the 4hrs target and removes the false dichotomy in which 3 hrs. 59 minutes is regarded as a success and 4 hrs. 1 minute a failure. - Inpatient Daily Discharge Ratio (DDR): This enables hospitals to predict capacity shortfalls and allows the wider healthcare system to intervene to ameliorate such situations. Low ratios are known to be associated with increased A&E waits the next day. - Using HES to assess length of stay for elective and non-elective patients by day of the week to form a key benefit in the 7DS in hospital impact assessment – this is key analysis would have been impossible without HES. This will feed into the Impact Assessment on 7 Day Services. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	This application is for online access to the record level HES database via the HDIS2 system. The system is hosted and audited by NHS Digital meaning that large transfers of data to on-site servers is reduced and NHS Digital has the ability to audit the use and access to the data. HDIS is accessed via a two-factor secure authentication method to approved users who are in receipt of an encryption token ID. Users have to attend training before the account is set up and users are only permitted to access the datasets that are agreed within this agreement. Users log onto the HDIS system and are presented with a SAS software application called Enterprise Guide which presents the users with a list of available data sets and available reference data tables so that they can return appropriate descriptions to the coded data. The access and use of the system is fully auditable and all users have to comply with the use of the data as specified in this agreement. The software tool also provides users with the ability to perform full data minimisation and filtering of the HES data as part of processing activities. Users are not permitted to upload data into the system. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users will have access to record level or aggregate data containing small numbers downloaded from the HDIS system. All HDIS users with access to the HDIS system are substantive employees of DH. Following completion of the analysis the record level data will be securely destroyed. DH currently has 24 licenses for access to HDIS and have the option to apply for further licenses if required. Approval for additional licences will be managed by the NHS Digital.	Due to the nature of the organisation, outputs are often unknown in advance and these will be driven by changing policy and ministerial priorities. Any outputs that are produced from the system that are to be published or shared with a third party (individuals or organisations outside of the analytical team) will be aggregated with small number suppressed in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. Below are some recent examples of the uses of HES data within DH: • Input to the quality assurance of denominator data derived from KH03 (quarterly bed availability and occupancy) used by PHE in annual publication of Healthcare Associated Infections (HCAI) rates. • Development of Alcohol Attributable fractions. It is anticipated that a similar approach might be used in future for new developing public health analyses. Through analysis of the data it is possible to calculate the cost of alcohol to the NHS which are carried out annually to support DH policy teams business case. A similar approach has been taken for smoking. • Research into areas of current policy interest, eg pneumonia. • As part of the New Models of Care and Transformation agendas (both SoS priorities), a key efficiency metric that will be used to measure success is bed days. DH has utilised HES data to understand this metric further, i.e. what variables in HES are used to calculate bed days, how good is the measure, etc. DH are currently using the data to explore some possible hypotheses such as: - Whether there are more bed days for patients admitted in the week vs. at the weekend; and - Under what treatment specialties are bed days very high, etc. None of this work so far has been used for official briefings or publications, but it is very likely that HES will be needed in the near future for briefings and QA. DH intend to utilise the HES data for other metrics for new models of care (NMC) and Transformation, for example A&E attendances and performance against the A&E 4-hour waiting standard. • DH works closely with DfE on policy for hospital schools. A new model of funding for hospital schools is being developed and HES data is playing an important role in this. • OECD research into Purchasing Power Parity in healthcare provision – An analysis is being carried out on the activity and prices for delivery of certain specific healthcare services (inpatient and day case basis). To do this access to HES data is required which details this at HRG level. • Cross sectional and time series analysis to understand efficiency and productivity of healthcare providers – This analysis is to be used for work relating to the Lord Carter report on efficiency, reporting on measures of efficiency and productivity for Secretary of State and HMT • Ministerial briefings - On-going work to understanding the link between activity/workload and staffing levels, work to understand impact upon safety and quality of care. • Internal analysis to provide management information required for the spending review.	The use of HDIS allows DH analysts to have a secure access to a remotely hosted software application for the analysis of HES data. Having access to record level downloads will permit the following activities which are not possible/practical within the HDIS system itself: - following individual patient pathways through each of the datasets - following individual patient pathways chronologically - permits linkage of HES data to anonymous data (e.g. Health Resource Group tariff information) The provision of this tool enables rapid analysis to be performed on the most recent version of the data. The availability of this function is crucial to DH in circumstances where speedy analysis is required to react to either local public health, commissioning or research requirements. Access to the data helps to Inform national policy development aimed at the improvement of patient outcomes generally.
DEPARTMENT OF HEALTH (DH)	DEPARTMENT OF HEALTH (DH)	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HDIS system enables organisations to access HES data for a wide range of data analytical purposes. The system is an online analytical processing tool through which the users of this organisation data has access to a wide range of analytical, graphical, statistical and reporting functions. Access is provided to the entire HES dataset (non-identifiable) for the specific purposes as listed below. The Department of Health will use the HDIS system through the analysis of HES data for the following purposes: - Benchmarking; - Provision of support services; - Producing publications including contributing to national and regional publications such as A7E reports; - Supporting the Government in the development and monitoring of policy; - Early analysis for projects and programmes to support commissioning and policy decisions; - Commissioning decisions; - Responding to and answering of parliamentary questions in a timely fashion as part of statutory duties. The analysis conducted by DH is wide ranging and will most often be used for internal DH purposes. DH analysts do however also provide support to other agencies including NHS England, PHE, NHS Blood and Transplant etc. Department of Health analytics team is sometimes under great pressure from No. 10 / Secretary of State (SofS) to provide statistics such as: • How many more operations (FCEs with a procedure or intervention) the NHS or individual providers are doing now compared to earlier years • Waiting times for common procedures such as hips, knees, cataracts in England compared to another devolved administration, usually Wales (with PEDW being the Welsh equivalent of HES) The analytics team carry out a project for the OECD to provide information on volumes and costs of specific procedures and groups of patients. The criteria used to determine which individual cases should or should not be included is fairly rigid and HES allows the criteria to be set to meet the requirements exactly. The data the team provide is used to create indicators of efficiency and productivity which are comparable on an international basis and are used for the “Health at a Glance” publication. A Recent example of HDIS use has been used to explore the determinants of emergency admissions from A&E from 2010 onwards. The research question was: are non-elective admissions from A&E driven only by demand-side factors (type and severity of condition)? Do supply-side factors (hospital capacity) matter? The team conducted this analysis as part of the value maps project: a piece of analysis HM Treasury commissioned from every central government Department in order to assess their understanding of current and potential efficiency and effectiveness. In terms of methodology, a logit model was used where the unit of observation was an A&E episode from 2010 onwards and the binary dependent variable described whether the episode ended up in a non-elective admission or not. This research project is currently on hold due to other emerging priorities however it is scheduled to be finalised after DH2020. The above project is an important example for the following reasons: (1) it was fundamental to have patient level data (as it was the only way to control for observable demand-side factors); (2) it was part of a high-profile piece of work (commissioned by (Director General of public spending and finance at HMT) and (Chief Economic Adviser at HMT), and presented to a panel of senior officials from prestigious organisations (Deputy National Statistician and Director General for Population and Public Policy at ONS) and (Chief Executive of the Behavioural Insights Team and Board Director)] Two further examples of how data are being used: a. Analysis of acute care data including bed days and emergency admissions to support the New Models of Care and Transformation programmes (both SofS priorities). Department Of Health rely on HES data to analyse time trends and local variation to feed into SoS Transformation meetings and other needs. b. Analysis of referrals to Outpatients – this has informed a range of policy work including extending the ability of AHPs to refer directly to Outpatient clinics, the savings potentially achievable by key interventions such as GP One Stop, local patterns of referral by demographics. Accident and Emergency is one of several compartments in the Model Hospital (MH). It has been developed by combining key indicators recommended by the Royal College of Emergency Medicine (RCEM) with productivity metrics recommended by Lord Carter operational productivity team. One of the purpose of the MH is to serve as a platform to enable Trusts to compare resource and associated clinical output, level of responsiveness as well as their overall financial productivity to that of their peers. Some of the indicators created using the data you provided are below: - % waiting <6 hours: RCEM opinion is that four other flow metrics in combination with the four hours standard waiting time performance metric are essential to optimizing the productivity of the emergency department. The ‘A&E 6hrs waiting time performance’ is one of the four metrics. - Aggregated Patient Delay (APD): This adds granularity to the 4hrs target and removes the false dichotomy in which 3 hrs. 59 minutes is regarded as a success and 4 hrs. 1 minute a failure. - Inpatient Daily Discharge Ratio (DDR): This enables hospitals to predict capacity shortfalls and allows the wider healthcare system to intervene to ameliorate such situations. Low ratios are known to be associated with increased A&E waits the next day. - Using HES to assess length of stay for elective and non-elective patients by day of the week to form a key benefit in the 7DS in hospital impact assessment – this is key analysis would have been impossible without HES. This will feed into the Impact Assessment on 7 Day Services. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	This application is for online access to the record level HES database via the HDIS2 system. The system is hosted and audited by NHS Digital meaning that large transfers of data to on-site servers is reduced and NHS Digital has the ability to audit the use and access to the data. HDIS is accessed via a two-factor secure authentication method to approved users who are in receipt of an encryption token ID. Users have to attend training before the account is set up and users are only permitted to access the datasets that are agreed within this agreement. Users log onto the HDIS system and are presented with a SAS software application called Enterprise Guide which presents the users with a list of available data sets and available reference data tables so that they can return appropriate descriptions to the coded data. The access and use of the system is fully auditable and all users have to comply with the use of the data as specified in this agreement. The software tool also provides users with the ability to perform full data minimisation and filtering of the HES data as part of processing activities. Users are not permitted to upload data into the system. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users will have access to record level or aggregate data containing small numbers downloaded from the HDIS system. All HDIS users with access to the HDIS system are substantive employees of DH. Following completion of the analysis the record level data will be securely destroyed. DH currently has 24 licenses for access to HDIS and have the option to apply for further licenses if required. Approval for additional licences will be managed by the NHS Digital.	Due to the nature of the organisation, outputs are often unknown in advance and these will be driven by changing policy and ministerial priorities. Any outputs that are produced from the system that are to be published or shared with a third party (individuals or organisations outside of the analytical team) will be aggregated with small number suppressed in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. Below are some recent examples of the uses of HES data within DH: • Input to the quality assurance of denominator data derived from KH03 (quarterly bed availability and occupancy) used by PHE in annual publication of Healthcare Associated Infections (HCAI) rates. • Development of Alcohol Attributable fractions. It is anticipated that a similar approach might be used in future for new developing public health analyses. Through analysis of the data it is possible to calculate the cost of alcohol to the NHS which are carried out annually to support DH policy teams business case. A similar approach has been taken for smoking. • Research into areas of current policy interest, eg pneumonia. • As part of the New Models of Care and Transformation agendas (both SoS priorities), a key efficiency metric that will be used to measure success is bed days. DH has utilised HES data to understand this metric further, i.e. what variables in HES are used to calculate bed days, how good is the measure, etc. DH are currently using the data to explore some possible hypotheses such as: - Whether there are more bed days for patients admitted in the week vs. at the weekend; and - Under what treatment specialties are bed days very high, etc. None of this work so far has been used for official briefings or publications, but it is very likely that HES will be needed in the near future for briefings and QA. DH intend to utilise the HES data for other metrics for new models of care (NMC) and Transformation, for example A&E attendances and performance against the A&E 4-hour waiting standard. • DH works closely with DfE on policy for hospital schools. A new model of funding for hospital schools is being developed and HES data is playing an important role in this. • OECD research into Purchasing Power Parity in healthcare provision – An analysis is being carried out on the activity and prices for delivery of certain specific healthcare services (inpatient and day case basis). To do this access to HES data is required which details this at HRG level. • Cross sectional and time series analysis to understand efficiency and productivity of healthcare providers – This analysis is to be used for work relating to the Lord Carter report on efficiency, reporting on measures of efficiency and productivity for Secretary of State and HMT • Ministerial briefings - On-going work to understanding the link between activity/workload and staffing levels, work to understand impact upon safety and quality of care. • Internal analysis to provide management information required for the spending review.	The use of HDIS allows DH analysts to have a secure access to a remotely hosted software application for the analysis of HES data. Having access to record level downloads will permit the following activities which are not possible/practical within the HDIS system itself: - following individual patient pathways through each of the datasets - following individual patient pathways chronologically - permits linkage of HES data to anonymous data (e.g. Health Resource Group tariff information) The provision of this tool enables rapid analysis to be performed on the most recent version of the data. The availability of this function is crucial to DH in circumstances where speedy analysis is required to react to either local public health, commissioning or research requirements. Access to the data helps to Inform national policy development aimed at the improvement of patient outcomes generally.
DEVICE ACCESS UK LTD	DEVICE ACCESS UK LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Overview of objective for processing: Device Access Limited conduct bespoke analysis on behalf of commercial companies, the NHS, academics and charities. This is a programme of work which identifies where medical and diagnostic technological devices can best be used by NHS providers in NHS patient care pathways to improve patient outcomes and to reduce lengths of stay, elective waiting times and diagnostic waiting times. This programme of work can be split into five distinct project areas as detailed below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd (DAUK) act as a bridge between medical technological device companies and the NHS by providing these companies with expertise and bespoke analysis in order to complete the NICE (National Institute of Clinical Excellence) process required before new innovations can be recommended and then adopted into the NHS for the benefit of patients. This activity is commissioned by the Medical Technology Companies, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. The device companies have no influence over the way in which the analysis is processed or the results. NICE programmes relating to the review of new or existing Medical and diagnostic technologies require a submission based on a detailed understanding of and real evidence around, current NHS patient pathways. NICE submissions offer relatively speedy assessments of selected innovations, with the aim of making relevant information available quickly to clinicians and NHS hospital decision makers, to support rapid adoption where this is appropriate. Submissions to NICE require the following which DAUK extract from HES datasets; • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and / or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Detailed understanding of cost-effectiveness improvements compared to current NHS pathways Before accepting a request from a commercial company and prior to any analysis or extraction of data, DAUK ensures that the technology meets the criteria of a potentially successful NICE submission by conducting internal research around the following formula (PICO); I. Population - Who would the technology treat II. Indication – how the technology works III. Comparator –How care is currently delivered to the population IV. Outcome – Are the benefits in line with the health and social care act 2014 Only once DAUK are satisfied that the technology meets these criteria and will be of benefit to patients and/or the health care system will they then conduct the bespoke analysis required for NICE submissions. As an example of this work, DAUK are currently providing bespoke analysis for a medical device company who have developed a non-invasive diagnostic technology which enables paramedics to diagnose stroke patients, and ensure they are sent to the relevant centres depending on the type of stroke. Currently in stroke diagnosis, paramedics are unable to determine whether the patient has had a minor stroke, which can be treated with thrombolysis drug therapy, or a major stroke or large vessel occlusion or LVO. LVO often requires a treatment called thrombectomy, which uses catheters to physically remove the clot from the brain which can only be performed in one of 27 specialist regional neuro centres. Recent research has shown a 73% reduction in disability over current therapy for the 8,000 patients diagnosed with large vessel occlusion every year. However, the most critical thing is that patients are triaged rapidly to these specialist centres and not an A&E in a District General Hospital who cannot perform the thrombectomy procedure. Delays not getting the right stroke intervention greatly affect patient outcomes. Through analysing HES with the Major Stroke centre network, DAUK are examining current stroke patient pathways, to understand how this technology will potentially impact and benefit patients and the NHS. The technology has the potential to improve patient outcomes for 44,000 patients a year in the UK that suffer from a stroke. DAUKs work will be used as part of the information required by the NICE submission process for this technology. 2. NIHR research applications on behalf of medical technological device companies: The objectives for processing the HES data are to provide bespoke analysis to medical technological device companies for their applications for research studies through the NIHR (National Institute for Health Research) . NIHR research is often required to provide clinical evidence for NICE applications. This activity is commissioned and funded by the Medical Technology Company, but can be funded directly to the Medical Technology Company though NIHR grants. The bespoke analysis provided by DAUK includes; • Clinical need analysis, evidence gap analysis and clinical endpoints • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Understanding of potential cost-effectiveness improvements compared to current NHS pathways Using the Stroke diagnostic tool as the example again, DAUK have provided the bespoke analysis which has secured the NIHR research. DAUK have also identified the areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd provide expertise and bespoke analysis to medical technological device companies and the NHS in order to assist with adoption and diffusion of NICE recommended technologies so new innovations can be adopted into the NHS for the benefit of patients, and the healthcare system. This analysis is used to support local NHS adoption by providing information relating to benefits for patients in a local area. This information is sent by Device Access to both the commissioning medical device company and also the NHS hospital stake holders to demonstrate the benefit to comply with NICE guidance. This activity is commissioned and paid for by the by the Medical Technology Company. DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be used. The device companies have no influence over the way in which the analysis is processed or the results. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provide expertise and bespoke analysis to the NHS in order to assist with identifying new medical technology and diagnostics in support of relevant disease states with a view that these will be adopted into the NHS for the benefit of patients, and NHS hospitals. This activity is commissioned by the NHS, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. This work does not include broad based economic consultancy services such as benchmarking but is limited to the purposes described in this document. DAUK would submit an amendment application to NHS Digital if DAUK were to consider a consultancy service which fell outside this agreed purpose. 5. Department for International Trade DIT and Office for Life Sciences: Since 2010, Device Access UK Ltd has supported the Department of International Trade or DIT, formerly known as the UK Trade and Investment (UKTI) who are part of the Government’s Office for Life Sciences (OLS). Part of the DIT and OLS objectives are to attract inward investment into the UK from companies across the world, to develop their business here in the life science sectors and DAUK has worked to support the strategy of the OLS and DIT. OLS and DIT commission DAUK with requests for market information, and DAUK provides free expertise on disease state, procedure volume and reimbursement. DAUK decide the manner and the process by which the analysis is carried out. DIT and OLS have no influence over the way in which the analysis is processed or the results.	Only substantive employees of Device Access UK Ltd have access to the data and only for the purposes described in this document. The data is not linked to any other data source or supplied to any third party. Processing occurs on a client by client basis and is bespoke to each client. Clients do not have access to any data via a portal. Processing activities are essentially the same for each project within the programme; a. DAUK use the HES data to perform the PICO – the analysis to check that the technology meets the criteria described and will be of benefit to patients and/or the healthcare system. b. DAUK analyse the HES data using industry standard algorithms and extensive expertise to extract the relevant cohort or population c. The analysis is used to produced bespoke reports and graphical charts which contain only aggregate data with small numbers suppressed in line with the HES Analysis guide d. Device Access produces HES information on disease state, procedure volume and reimbursement analysis. This analysis is at hospital or provider level, to ensure regional differences and variances in care pathways are analysed. However for project 5 this will only be at a national level.	Commercial companies specifically agree not to use the analysis provided by DAUK within general marketing, or within collateral material used by sales and marketing teams. These include sales brochures, emails, direct or mass mailing and advertising of medical technologies. Device Access Ltd do not allow portal access to the data by clients. Description of outputs have been provided below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions to NICE for evaluation. These bespoke outputs come in the form of reports and graphical charts and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NICE applications. Below are examples of DAUK analysis which has been used in NICE applications and have resulted in positive NICE guidance in the last 12 months; NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis NICE IPG549 Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death. NICE IPG578 Minimally invasive sacroiliac joint fusion surgery for chronic sacroiliac pain Prior to 2016, Device Access UK Ltd supported 10 additional NICE Medical Technology approvals leading to guidance; NICE MT 241 Device for relief of benign prostate hyperplasia NICE MIB 47 Diagnosis Dry Eye NICE TA 628 Intraoperative Radiotherapy for Breast Cancer NICE MIB 8 Airsonett temperature-controlled laminar airflow device for persistent allergic asthma NICE IPG 479 Device to manage ascities NICE IP 475 Device for relief of benign prostate hyperplasia NICE DG 12 Diagnostic Test To Manage Asthma NICE IPG 431 Laparoscopic insertion of a magnetic bead band for gastro-oesophageal reflux disease NICE IPG 396 Trabecular stent bypass microsurgery for open angle glaucoma 2. NIHR research applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions for NIHR funding. All bespoke outputs come in the form of reports and graphical charts and are aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NIHR applications. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with adoption of NICE approved technologies into the NHS. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used as evidence to support discussions in NHS Hospitals with key NHS stakeholders including finance directors, clinical managers, clinicians and CCG’s. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd uses the HES analysis to support their NHS clients with the development of new medical technology and diagnostics in support of relevant disease states. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is used as evidence to support medical technology developments in NHS Hospitals with key NHS stakeholders including commissioners, clinicians and researchers 5. Department for International Trade DIT and Office for Life Sciences (OLS): A basic summary of National HES extracts of disease state, procedure volume and reimbursement analysis is supplied free of charge by DAUK to the DIT and OLS for use in direct and combined discussions with potential overseas companies considering UK market entry. DAUK only provides DIT and OLS with National information, not by hospital or provider. Outputs are therefore aggregated, with small numbers suppressed in line with the HES Analysis Guide.	1. NICE applications on behalf of medical technological device companies: The transformation programme of the NHS recognises the importance of supporting innovation and enabling the fast adoption of cost-effective new technologies to secure improved patient outcomes. Device Access UK Ltd are recognised by medical device companies and NICE as experts in the use of HES data to provide robust and trusted analysis which allows the NHS to assess the impact of medical innovation on patients and the health care system. Without the work DAUK provide, innovation could be delayed in the NICE process and this would have a negative impact on the time it would take for the benefits of such technologies to reach patients and the NHS. Two examples of the benefits to the health and social care derived from the data supplied by Device Access UK Ltd and NICE approvals are; I. Benign prostate hyperplasia (BPH). Current NHS Treatments result in an over 2-day length of stay, resulting in over 45,000 bed days. Through data analysis, Device Access helped identify an opportunity for these procedures to be performed on a day case basis supporting best practice, benefiting the patient and making cost savings for the NHS. View the NICE news release here https://www.nice.org.uk/news/article/thousands-of-men-with-enlarged-prostates-could-be-helped-by-new-nice-guidance-on-laser-device II. Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death http://www.bbc.co.uk/news/health-32056350 2. NIHR research applications on behalf of medical technological device companies: NICE often advise medical device companies that further clinical research would be required as part of the NICE submission process. DAUK provide accurate and trusted bespoke analysis for the medical technological device companies to complete the NIHR evaluation. As with objective 1, DAUK’s work means that NIHR can assess sound analysis helping them evaluate quickly whether further research would be worthwhile. The DAUK analysis also identifies areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. It ensures potential evidence gaps are eliminated and through research with the NIHR, help the NHS determine how to roll out innovations for the benefit of patients. 3. NHS adoption support on behalf of medical technological device companies: The NHS is a highly complex and fragmented system. The infrastructure to rapidly and efficiently spread medical innovations, in a way that ensures that every patient in the NHS benefits no matter which hospital or which GP practice they visit, does not yet exist. The work of DAUK contributes to the process of helping these innovations reach and get adopted by the NHS in a more efficient way for the benefit of patients and the healthcare system. Below are 2 examples of NICE approved technologies Device Access is supporting following NICE evaluation within the last 12 months. In the examples, Device Access have provided individual Hospitals directly with information relating to length of stay and bespoke analysis around the cost benefit for adoption which has assisted decision makers in NHS hospitals to commission new care pathways to provide a day case setting for these patients suffering from benign prostate hyperplasia and chronic sinusitis. This has resulted in faster NHS uptake for the technology and clear benefits for these patients. i. NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis- XprESS multi sinus dilation system for treating uncomplicated chronic sinusitis after medical treatment has failed is supported by NICE. Treatment with XprESS leads to a rapid and sustained improvement for the patient in chronic symptoms, fewer acute episodes and improved quality of life which is comparable to functional endoscopic sinus surgery (FESS). This treatment is of further benefit to the patient by having faster post treatment recovery times, and can more often be done under local anaesthesia. Cost modelling indicates that XprESS is cost saving compared with FESS when treatment is done using local anaesthetic in an outpatient setting. By adopting this technology, the NHS in England may save around £7.4 million a year by 2020. Estimated savings are mainly achieved through the shift of treatment from operating theatre to outpatient setting. ii. NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia- GreenLight XPS for treating benign prostatic hyperplasia is supported by NICE in non-high-risk patients. GreenLight XPS is at least as effective in these patients as transurethral resection of the prostate (TURP), but can more often be done as a day-case procedure and is far less invasive for the benefit of the patient. Cost modelling indicates that in non-high-risk patients, cost savings with GreenLight XPS compared with TURP are determined by the proportion of procedures done as day cases. NICE's resource impact report estimates that the annual cost saving for the NHS in England is around £2.3 million. In a plausible scenario of 70% of treatments being done as day cases, the cost saving may be up to £3.2 million. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provides bespoke analysis to support NHS clients in their development of medical innovation for the benefit of their patients, local healthcare setting and/or the wider healthcare system. Examples of bespoke DAUK analysis provided to NHS clients are as follows; I. Facial palsy; Device Access worked with clinicians at East Grinstead Hospital who are developing a new rehabilitation system for patients with facial palsy as well as looking at how this illness can be prevented. To support this work, Device Access supplied analysis relating to the prevalence of Facial palsy and lymes disease. This analysis allowed the clinicians and the facial palsy charity to raise National awareness of the condition and look at developing new rehabilitation therapies across the country, as East Grinstead is currently the only NHS centre offering help for these patients. This new technology is being developed by the team at East Grinstead. https://www.youtube.com/watch?v=3MA_F7nIxEY&feature=youtu.be II. Gastro oesophageal reflux disease (GORD) research – understanding 4 Year patient outcomes from Antireflux fundoplication procedures; Device Access are currently working with Epsom and St Helier NHS Trust and supporting a paper to explain the impacts of the current standard of care for gastro oesophageal reflux disease surgery. The standard of care for these patients from a surgical perspective is a laparoscopic treatment called nissen fundoplication. DAUK HES analysis has shown that approximately 10% of these treatments result in further revision surgery. This research paper will be looking at those patients requiring revision surgery and those not in the relevant control group. Looking at risk factors impacting on the condition along with the cost and resource used to treat this group of patients. This will result in a new way of managing these patients and improving patient outcomes through the introduction of an alternative treatment through raising awareness of the current outcomes. This work was commissioned by the hospital but is being funded by a Medical Technology company who are developing a new therapy for the treatment of GORD. III. NHS Improvement; NHS Improvement recently approached Device Access for national information on the cause and incidence of pressure ulcers across NHS England. They wanted to understand what medical device technology is currently available and what impact these devices could have on the cost of pressure ulcers and patient care. 10% of all pressure ulcers that are treated in hospitals are community acquired with the remaining being acquired in hospital. The bespoke analysis completed by DAUK has provided NHS Improvement with an understanding of the most common reasons for admission leading to pressure ulcers. This analysis has been provided to NHS improvement at no cost. http://nhs.stopthepressure.co.uk 5. Department for International Trade DIT and Office for Life Sciences (OLS): This free service of providing the OLS and DIT with expertise around the NHS Market size has over the years encouraged several overseas companies to inwardly invest in the United Kingdom Market. This helps keep the UK as one of the world’s best places for inward investment, the NHS ahead in research, evaluation and adoption of innovative medical technologies, allowing patients to benefit from new innovative treatments sooner.
DEVICE ACCESS UK LTD	DEVICE ACCESS UK LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Overview of objective for processing: Device Access Limited conduct bespoke analysis on behalf of commercial companies, the NHS, academics and charities. This is a programme of work which identifies where medical and diagnostic technological devices can best be used by NHS providers in NHS patient care pathways to improve patient outcomes and to reduce lengths of stay, elective waiting times and diagnostic waiting times. This programme of work can be split into five distinct project areas as detailed below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd (DAUK) act as a bridge between medical technological device companies and the NHS by providing these companies with expertise and bespoke analysis in order to complete the NICE (National Institute of Clinical Excellence) process required before new innovations can be recommended and then adopted into the NHS for the benefit of patients. This activity is commissioned by the Medical Technology Companies, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. The device companies have no influence over the way in which the analysis is processed or the results. NICE programmes relating to the review of new or existing Medical and diagnostic technologies require a submission based on a detailed understanding of and real evidence around, current NHS patient pathways. NICE submissions offer relatively speedy assessments of selected innovations, with the aim of making relevant information available quickly to clinicians and NHS hospital decision makers, to support rapid adoption where this is appropriate. Submissions to NICE require the following which DAUK extract from HES datasets; • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and / or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Detailed understanding of cost-effectiveness improvements compared to current NHS pathways Before accepting a request from a commercial company and prior to any analysis or extraction of data, DAUK ensures that the technology meets the criteria of a potentially successful NICE submission by conducting internal research around the following formula (PICO); I. Population - Who would the technology treat II. Indication – how the technology works III. Comparator –How care is currently delivered to the population IV. Outcome – Are the benefits in line with the health and social care act 2014 Only once DAUK are satisfied that the technology meets these criteria and will be of benefit to patients and/or the health care system will they then conduct the bespoke analysis required for NICE submissions. As an example of this work, DAUK are currently providing bespoke analysis for a medical device company who have developed a non-invasive diagnostic technology which enables paramedics to diagnose stroke patients, and ensure they are sent to the relevant centres depending on the type of stroke. Currently in stroke diagnosis, paramedics are unable to determine whether the patient has had a minor stroke, which can be treated with thrombolysis drug therapy, or a major stroke or large vessel occlusion or LVO. LVO often requires a treatment called thrombectomy, which uses catheters to physically remove the clot from the brain which can only be performed in one of 27 specialist regional neuro centres. Recent research has shown a 73% reduction in disability over current therapy for the 8,000 patients diagnosed with large vessel occlusion every year. However, the most critical thing is that patients are triaged rapidly to these specialist centres and not an A&E in a District General Hospital who cannot perform the thrombectomy procedure. Delays not getting the right stroke intervention greatly affect patient outcomes. Through analysing HES with the Major Stroke centre network, DAUK are examining current stroke patient pathways, to understand how this technology will potentially impact and benefit patients and the NHS. The technology has the potential to improve patient outcomes for 44,000 patients a year in the UK that suffer from a stroke. DAUKs work will be used as part of the information required by the NICE submission process for this technology. 2. NIHR research applications on behalf of medical technological device companies: The objectives for processing the HES data are to provide bespoke analysis to medical technological device companies for their applications for research studies through the NIHR (National Institute for Health Research) . NIHR research is often required to provide clinical evidence for NICE applications. This activity is commissioned and funded by the Medical Technology Company, but can be funded directly to the Medical Technology Company though NIHR grants. The bespoke analysis provided by DAUK includes; • Clinical need analysis, evidence gap analysis and clinical endpoints • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Understanding of potential cost-effectiveness improvements compared to current NHS pathways Using the Stroke diagnostic tool as the example again, DAUK have provided the bespoke analysis which has secured the NIHR research. DAUK have also identified the areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd provide expertise and bespoke analysis to medical technological device companies and the NHS in order to assist with adoption and diffusion of NICE recommended technologies so new innovations can be adopted into the NHS for the benefit of patients, and the healthcare system. This analysis is used to support local NHS adoption by providing information relating to benefits for patients in a local area. This information is sent by Device Access to both the commissioning medical device company and also the NHS hospital stake holders to demonstrate the benefit to comply with NICE guidance. This activity is commissioned and paid for by the by the Medical Technology Company. DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be used. The device companies have no influence over the way in which the analysis is processed or the results. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provide expertise and bespoke analysis to the NHS in order to assist with identifying new medical technology and diagnostics in support of relevant disease states with a view that these will be adopted into the NHS for the benefit of patients, and NHS hospitals. This activity is commissioned by the NHS, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. This work does not include broad based economic consultancy services such as benchmarking but is limited to the purposes described in this document. DAUK would submit an amendment application to NHS Digital if DAUK were to consider a consultancy service which fell outside this agreed purpose. 5. Department for International Trade DIT and Office for Life Sciences: Since 2010, Device Access UK Ltd has supported the Department of International Trade or DIT, formerly known as the UK Trade and Investment (UKTI) who are part of the Government’s Office for Life Sciences (OLS). Part of the DIT and OLS objectives are to attract inward investment into the UK from companies across the world, to develop their business here in the life science sectors and DAUK has worked to support the strategy of the OLS and DIT. OLS and DIT commission DAUK with requests for market information, and DAUK provides free expertise on disease state, procedure volume and reimbursement. DAUK decide the manner and the process by which the analysis is carried out. DIT and OLS have no influence over the way in which the analysis is processed or the results.	Only substantive employees of Device Access UK Ltd have access to the data and only for the purposes described in this document. The data is not linked to any other data source or supplied to any third party. Processing occurs on a client by client basis and is bespoke to each client. Clients do not have access to any data via a portal. Processing activities are essentially the same for each project within the programme; a. DAUK use the HES data to perform the PICO – the analysis to check that the technology meets the criteria described and will be of benefit to patients and/or the healthcare system. b. DAUK analyse the HES data using industry standard algorithms and extensive expertise to extract the relevant cohort or population c. The analysis is used to produced bespoke reports and graphical charts which contain only aggregate data with small numbers suppressed in line with the HES Analysis guide d. Device Access produces HES information on disease state, procedure volume and reimbursement analysis. This analysis is at hospital or provider level, to ensure regional differences and variances in care pathways are analysed. However for project 5 this will only be at a national level.	Commercial companies specifically agree not to use the analysis provided by DAUK within general marketing, or within collateral material used by sales and marketing teams. These include sales brochures, emails, direct or mass mailing and advertising of medical technologies. Device Access Ltd do not allow portal access to the data by clients. Description of outputs have been provided below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions to NICE for evaluation. These bespoke outputs come in the form of reports and graphical charts and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NICE applications. Below are examples of DAUK analysis which has been used in NICE applications and have resulted in positive NICE guidance in the last 12 months; NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis NICE IPG549 Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death. NICE IPG578 Minimally invasive sacroiliac joint fusion surgery for chronic sacroiliac pain Prior to 2016, Device Access UK Ltd supported 10 additional NICE Medical Technology approvals leading to guidance; NICE MT 241 Device for relief of benign prostate hyperplasia NICE MIB 47 Diagnosis Dry Eye NICE TA 628 Intraoperative Radiotherapy for Breast Cancer NICE MIB 8 Airsonett temperature-controlled laminar airflow device for persistent allergic asthma NICE IPG 479 Device to manage ascities NICE IP 475 Device for relief of benign prostate hyperplasia NICE DG 12 Diagnostic Test To Manage Asthma NICE IPG 431 Laparoscopic insertion of a magnetic bead band for gastro-oesophageal reflux disease NICE IPG 396 Trabecular stent bypass microsurgery for open angle glaucoma 2. NIHR research applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions for NIHR funding. All bespoke outputs come in the form of reports and graphical charts and are aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NIHR applications. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with adoption of NICE approved technologies into the NHS. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used as evidence to support discussions in NHS Hospitals with key NHS stakeholders including finance directors, clinical managers, clinicians and CCG’s. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd uses the HES analysis to support their NHS clients with the development of new medical technology and diagnostics in support of relevant disease states. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is used as evidence to support medical technology developments in NHS Hospitals with key NHS stakeholders including commissioners, clinicians and researchers 5. Department for International Trade DIT and Office for Life Sciences (OLS): A basic summary of National HES extracts of disease state, procedure volume and reimbursement analysis is supplied free of charge by DAUK to the DIT and OLS for use in direct and combined discussions with potential overseas companies considering UK market entry. DAUK only provides DIT and OLS with National information, not by hospital or provider. Outputs are therefore aggregated, with small numbers suppressed in line with the HES Analysis Guide.	1. NICE applications on behalf of medical technological device companies: The transformation programme of the NHS recognises the importance of supporting innovation and enabling the fast adoption of cost-effective new technologies to secure improved patient outcomes. Device Access UK Ltd are recognised by medical device companies and NICE as experts in the use of HES data to provide robust and trusted analysis which allows the NHS to assess the impact of medical innovation on patients and the health care system. Without the work DAUK provide, innovation could be delayed in the NICE process and this would have a negative impact on the time it would take for the benefits of such technologies to reach patients and the NHS. Two examples of the benefits to the health and social care derived from the data supplied by Device Access UK Ltd and NICE approvals are; I. Benign prostate hyperplasia (BPH). Current NHS Treatments result in an over 2-day length of stay, resulting in over 45,000 bed days. Through data analysis, Device Access helped identify an opportunity for these procedures to be performed on a day case basis supporting best practice, benefiting the patient and making cost savings for the NHS. View the NICE news release here https://www.nice.org.uk/news/article/thousands-of-men-with-enlarged-prostates-could-be-helped-by-new-nice-guidance-on-laser-device II. Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death http://www.bbc.co.uk/news/health-32056350 2. NIHR research applications on behalf of medical technological device companies: NICE often advise medical device companies that further clinical research would be required as part of the NICE submission process. DAUK provide accurate and trusted bespoke analysis for the medical technological device companies to complete the NIHR evaluation. As with objective 1, DAUK’s work means that NIHR can assess sound analysis helping them evaluate quickly whether further research would be worthwhile. The DAUK analysis also identifies areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. It ensures potential evidence gaps are eliminated and through research with the NIHR, help the NHS determine how to roll out innovations for the benefit of patients. 3. NHS adoption support on behalf of medical technological device companies: The NHS is a highly complex and fragmented system. The infrastructure to rapidly and efficiently spread medical innovations, in a way that ensures that every patient in the NHS benefits no matter which hospital or which GP practice they visit, does not yet exist. The work of DAUK contributes to the process of helping these innovations reach and get adopted by the NHS in a more efficient way for the benefit of patients and the healthcare system. Below are 2 examples of NICE approved technologies Device Access is supporting following NICE evaluation within the last 12 months. In the examples, Device Access have provided individual Hospitals directly with information relating to length of stay and bespoke analysis around the cost benefit for adoption which has assisted decision makers in NHS hospitals to commission new care pathways to provide a day case setting for these patients suffering from benign prostate hyperplasia and chronic sinusitis. This has resulted in faster NHS uptake for the technology and clear benefits for these patients. i. NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis- XprESS multi sinus dilation system for treating uncomplicated chronic sinusitis after medical treatment has failed is supported by NICE. Treatment with XprESS leads to a rapid and sustained improvement for the patient in chronic symptoms, fewer acute episodes and improved quality of life which is comparable to functional endoscopic sinus surgery (FESS). This treatment is of further benefit to the patient by having faster post treatment recovery times, and can more often be done under local anaesthesia. Cost modelling indicates that XprESS is cost saving compared with FESS when treatment is done using local anaesthetic in an outpatient setting. By adopting this technology, the NHS in England may save around £7.4 million a year by 2020. Estimated savings are mainly achieved through the shift of treatment from operating theatre to outpatient setting. ii. NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia- GreenLight XPS for treating benign prostatic hyperplasia is supported by NICE in non-high-risk patients. GreenLight XPS is at least as effective in these patients as transurethral resection of the prostate (TURP), but can more often be done as a day-case procedure and is far less invasive for the benefit of the patient. Cost modelling indicates that in non-high-risk patients, cost savings with GreenLight XPS compared with TURP are determined by the proportion of procedures done as day cases. NICE's resource impact report estimates that the annual cost saving for the NHS in England is around £2.3 million. In a plausible scenario of 70% of treatments being done as day cases, the cost saving may be up to £3.2 million. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provides bespoke analysis to support NHS clients in their development of medical innovation for the benefit of their patients, local healthcare setting and/or the wider healthcare system. Examples of bespoke DAUK analysis provided to NHS clients are as follows; I. Facial palsy; Device Access worked with clinicians at East Grinstead Hospital who are developing a new rehabilitation system for patients with facial palsy as well as looking at how this illness can be prevented. To support this work, Device Access supplied analysis relating to the prevalence of Facial palsy and lymes disease. This analysis allowed the clinicians and the facial palsy charity to raise National awareness of the condition and look at developing new rehabilitation therapies across the country, as East Grinstead is currently the only NHS centre offering help for these patients. This new technology is being developed by the team at East Grinstead. https://www.youtube.com/watch?v=3MA_F7nIxEY&feature=youtu.be II. Gastro oesophageal reflux disease (GORD) research – understanding 4 Year patient outcomes from Antireflux fundoplication procedures; Device Access are currently working with Epsom and St Helier NHS Trust and supporting a paper to explain the impacts of the current standard of care for gastro oesophageal reflux disease surgery. The standard of care for these patients from a surgical perspective is a laparoscopic treatment called nissen fundoplication. DAUK HES analysis has shown that approximately 10% of these treatments result in further revision surgery. This research paper will be looking at those patients requiring revision surgery and those not in the relevant control group. Looking at risk factors impacting on the condition along with the cost and resource used to treat this group of patients. This will result in a new way of managing these patients and improving patient outcomes through the introduction of an alternative treatment through raising awareness of the current outcomes. This work was commissioned by the hospital but is being funded by a Medical Technology company who are developing a new therapy for the treatment of GORD. III. NHS Improvement; NHS Improvement recently approached Device Access for national information on the cause and incidence of pressure ulcers across NHS England. They wanted to understand what medical device technology is currently available and what impact these devices could have on the cost of pressure ulcers and patient care. 10% of all pressure ulcers that are treated in hospitals are community acquired with the remaining being acquired in hospital. The bespoke analysis completed by DAUK has provided NHS Improvement with an understanding of the most common reasons for admission leading to pressure ulcers. This analysis has been provided to NHS improvement at no cost. http://nhs.stopthepressure.co.uk 5. Department for International Trade DIT and Office for Life Sciences (OLS): This free service of providing the OLS and DIT with expertise around the NHS Market size has over the years encouraged several overseas companies to inwardly invest in the United Kingdom Market. This helps keep the UK as one of the world’s best places for inward investment, the NHS ahead in research, evaluation and adoption of innovative medical technologies, allowing patients to benefit from new innovative treatments sooner.
DEVICE ACCESS UK LTD	DEVICE ACCESS UK LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Overview of objective for processing: Device Access Limited conduct bespoke analysis on behalf of commercial companies, the NHS, academics and charities. This is a programme of work which identifies where medical and diagnostic technological devices can best be used by NHS providers in NHS patient care pathways to improve patient outcomes and to reduce lengths of stay, elective waiting times and diagnostic waiting times. This programme of work can be split into five distinct project areas as detailed below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd (DAUK) act as a bridge between medical technological device companies and the NHS by providing these companies with expertise and bespoke analysis in order to complete the NICE (National Institute of Clinical Excellence) process required before new innovations can be recommended and then adopted into the NHS for the benefit of patients. This activity is commissioned by the Medical Technology Companies, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. The device companies have no influence over the way in which the analysis is processed or the results. NICE programmes relating to the review of new or existing Medical and diagnostic technologies require a submission based on a detailed understanding of and real evidence around, current NHS patient pathways. NICE submissions offer relatively speedy assessments of selected innovations, with the aim of making relevant information available quickly to clinicians and NHS hospital decision makers, to support rapid adoption where this is appropriate. Submissions to NICE require the following which DAUK extract from HES datasets; • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and / or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Detailed understanding of cost-effectiveness improvements compared to current NHS pathways Before accepting a request from a commercial company and prior to any analysis or extraction of data, DAUK ensures that the technology meets the criteria of a potentially successful NICE submission by conducting internal research around the following formula (PICO); I. Population - Who would the technology treat II. Indication – how the technology works III. Comparator –How care is currently delivered to the population IV. Outcome – Are the benefits in line with the health and social care act 2014 Only once DAUK are satisfied that the technology meets these criteria and will be of benefit to patients and/or the health care system will they then conduct the bespoke analysis required for NICE submissions. As an example of this work, DAUK are currently providing bespoke analysis for a medical device company who have developed a non-invasive diagnostic technology which enables paramedics to diagnose stroke patients, and ensure they are sent to the relevant centres depending on the type of stroke. Currently in stroke diagnosis, paramedics are unable to determine whether the patient has had a minor stroke, which can be treated with thrombolysis drug therapy, or a major stroke or large vessel occlusion or LVO. LVO often requires a treatment called thrombectomy, which uses catheters to physically remove the clot from the brain which can only be performed in one of 27 specialist regional neuro centres. Recent research has shown a 73% reduction in disability over current therapy for the 8,000 patients diagnosed with large vessel occlusion every year. However, the most critical thing is that patients are triaged rapidly to these specialist centres and not an A&E in a District General Hospital who cannot perform the thrombectomy procedure. Delays not getting the right stroke intervention greatly affect patient outcomes. Through analysing HES with the Major Stroke centre network, DAUK are examining current stroke patient pathways, to understand how this technology will potentially impact and benefit patients and the NHS. The technology has the potential to improve patient outcomes for 44,000 patients a year in the UK that suffer from a stroke. DAUKs work will be used as part of the information required by the NICE submission process for this technology. 2. NIHR research applications on behalf of medical technological device companies: The objectives for processing the HES data are to provide bespoke analysis to medical technological device companies for their applications for research studies through the NIHR (National Institute for Health Research) . NIHR research is often required to provide clinical evidence for NICE applications. This activity is commissioned and funded by the Medical Technology Company, but can be funded directly to the Medical Technology Company though NIHR grants. The bespoke analysis provided by DAUK includes; • Clinical need analysis, evidence gap analysis and clinical endpoints • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Understanding of potential cost-effectiveness improvements compared to current NHS pathways Using the Stroke diagnostic tool as the example again, DAUK have provided the bespoke analysis which has secured the NIHR research. DAUK have also identified the areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd provide expertise and bespoke analysis to medical technological device companies and the NHS in order to assist with adoption and diffusion of NICE recommended technologies so new innovations can be adopted into the NHS for the benefit of patients, and the healthcare system. This analysis is used to support local NHS adoption by providing information relating to benefits for patients in a local area. This information is sent by Device Access to both the commissioning medical device company and also the NHS hospital stake holders to demonstrate the benefit to comply with NICE guidance. This activity is commissioned and paid for by the by the Medical Technology Company. DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be used. The device companies have no influence over the way in which the analysis is processed or the results. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provide expertise and bespoke analysis to the NHS in order to assist with identifying new medical technology and diagnostics in support of relevant disease states with a view that these will be adopted into the NHS for the benefit of patients, and NHS hospitals. This activity is commissioned by the NHS, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. This work does not include broad based economic consultancy services such as benchmarking but is limited to the purposes described in this document. DAUK would submit an amendment application to NHS Digital if DAUK were to consider a consultancy service which fell outside this agreed purpose. 5. Department for International Trade DIT and Office for Life Sciences: Since 2010, Device Access UK Ltd has supported the Department of International Trade or DIT, formerly known as the UK Trade and Investment (UKTI) who are part of the Government’s Office for Life Sciences (OLS). Part of the DIT and OLS objectives are to attract inward investment into the UK from companies across the world, to develop their business here in the life science sectors and DAUK has worked to support the strategy of the OLS and DIT. OLS and DIT commission DAUK with requests for market information, and DAUK provides free expertise on disease state, procedure volume and reimbursement. DAUK decide the manner and the process by which the analysis is carried out. DIT and OLS have no influence over the way in which the analysis is processed or the results.	Only substantive employees of Device Access UK Ltd have access to the data and only for the purposes described in this document. The data is not linked to any other data source or supplied to any third party. Processing occurs on a client by client basis and is bespoke to each client. Clients do not have access to any data via a portal. Processing activities are essentially the same for each project within the programme; a. DAUK use the HES data to perform the PICO – the analysis to check that the technology meets the criteria described and will be of benefit to patients and/or the healthcare system. b. DAUK analyse the HES data using industry standard algorithms and extensive expertise to extract the relevant cohort or population c. The analysis is used to produced bespoke reports and graphical charts which contain only aggregate data with small numbers suppressed in line with the HES Analysis guide d. Device Access produces HES information on disease state, procedure volume and reimbursement analysis. This analysis is at hospital or provider level, to ensure regional differences and variances in care pathways are analysed. However for project 5 this will only be at a national level.	Commercial companies specifically agree not to use the analysis provided by DAUK within general marketing, or within collateral material used by sales and marketing teams. These include sales brochures, emails, direct or mass mailing and advertising of medical technologies. Device Access Ltd do not allow portal access to the data by clients. Description of outputs have been provided below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions to NICE for evaluation. These bespoke outputs come in the form of reports and graphical charts and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NICE applications. Below are examples of DAUK analysis which has been used in NICE applications and have resulted in positive NICE guidance in the last 12 months; NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis NICE IPG549 Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death. NICE IPG578 Minimally invasive sacroiliac joint fusion surgery for chronic sacroiliac pain Prior to 2016, Device Access UK Ltd supported 10 additional NICE Medical Technology approvals leading to guidance; NICE MT 241 Device for relief of benign prostate hyperplasia NICE MIB 47 Diagnosis Dry Eye NICE TA 628 Intraoperative Radiotherapy for Breast Cancer NICE MIB 8 Airsonett temperature-controlled laminar airflow device for persistent allergic asthma NICE IPG 479 Device to manage ascities NICE IP 475 Device for relief of benign prostate hyperplasia NICE DG 12 Diagnostic Test To Manage Asthma NICE IPG 431 Laparoscopic insertion of a magnetic bead band for gastro-oesophageal reflux disease NICE IPG 396 Trabecular stent bypass microsurgery for open angle glaucoma 2. NIHR research applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions for NIHR funding. All bespoke outputs come in the form of reports and graphical charts and are aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NIHR applications. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with adoption of NICE approved technologies into the NHS. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used as evidence to support discussions in NHS Hospitals with key NHS stakeholders including finance directors, clinical managers, clinicians and CCG’s. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd uses the HES analysis to support their NHS clients with the development of new medical technology and diagnostics in support of relevant disease states. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is used as evidence to support medical technology developments in NHS Hospitals with key NHS stakeholders including commissioners, clinicians and researchers 5. Department for International Trade DIT and Office for Life Sciences (OLS): A basic summary of National HES extracts of disease state, procedure volume and reimbursement analysis is supplied free of charge by DAUK to the DIT and OLS for use in direct and combined discussions with potential overseas companies considering UK market entry. DAUK only provides DIT and OLS with National information, not by hospital or provider. Outputs are therefore aggregated, with small numbers suppressed in line with the HES Analysis Guide.	1. NICE applications on behalf of medical technological device companies: The transformation programme of the NHS recognises the importance of supporting innovation and enabling the fast adoption of cost-effective new technologies to secure improved patient outcomes. Device Access UK Ltd are recognised by medical device companies and NICE as experts in the use of HES data to provide robust and trusted analysis which allows the NHS to assess the impact of medical innovation on patients and the health care system. Without the work DAUK provide, innovation could be delayed in the NICE process and this would have a negative impact on the time it would take for the benefits of such technologies to reach patients and the NHS. Two examples of the benefits to the health and social care derived from the data supplied by Device Access UK Ltd and NICE approvals are; I. Benign prostate hyperplasia (BPH). Current NHS Treatments result in an over 2-day length of stay, resulting in over 45,000 bed days. Through data analysis, Device Access helped identify an opportunity for these procedures to be performed on a day case basis supporting best practice, benefiting the patient and making cost savings for the NHS. View the NICE news release here https://www.nice.org.uk/news/article/thousands-of-men-with-enlarged-prostates-could-be-helped-by-new-nice-guidance-on-laser-device II. Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death http://www.bbc.co.uk/news/health-32056350 2. NIHR research applications on behalf of medical technological device companies: NICE often advise medical device companies that further clinical research would be required as part of the NICE submission process. DAUK provide accurate and trusted bespoke analysis for the medical technological device companies to complete the NIHR evaluation. As with objective 1, DAUK’s work means that NIHR can assess sound analysis helping them evaluate quickly whether further research would be worthwhile. The DAUK analysis also identifies areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. It ensures potential evidence gaps are eliminated and through research with the NIHR, help the NHS determine how to roll out innovations for the benefit of patients. 3. NHS adoption support on behalf of medical technological device companies: The NHS is a highly complex and fragmented system. The infrastructure to rapidly and efficiently spread medical innovations, in a way that ensures that every patient in the NHS benefits no matter which hospital or which GP practice they visit, does not yet exist. The work of DAUK contributes to the process of helping these innovations reach and get adopted by the NHS in a more efficient way for the benefit of patients and the healthcare system. Below are 2 examples of NICE approved technologies Device Access is supporting following NICE evaluation within the last 12 months. In the examples, Device Access have provided individual Hospitals directly with information relating to length of stay and bespoke analysis around the cost benefit for adoption which has assisted decision makers in NHS hospitals to commission new care pathways to provide a day case setting for these patients suffering from benign prostate hyperplasia and chronic sinusitis. This has resulted in faster NHS uptake for the technology and clear benefits for these patients. i. NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis- XprESS multi sinus dilation system for treating uncomplicated chronic sinusitis after medical treatment has failed is supported by NICE. Treatment with XprESS leads to a rapid and sustained improvement for the patient in chronic symptoms, fewer acute episodes and improved quality of life which is comparable to functional endoscopic sinus surgery (FESS). This treatment is of further benefit to the patient by having faster post treatment recovery times, and can more often be done under local anaesthesia. Cost modelling indicates that XprESS is cost saving compared with FESS when treatment is done using local anaesthetic in an outpatient setting. By adopting this technology, the NHS in England may save around £7.4 million a year by 2020. Estimated savings are mainly achieved through the shift of treatment from operating theatre to outpatient setting. ii. NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia- GreenLight XPS for treating benign prostatic hyperplasia is supported by NICE in non-high-risk patients. GreenLight XPS is at least as effective in these patients as transurethral resection of the prostate (TURP), but can more often be done as a day-case procedure and is far less invasive for the benefit of the patient. Cost modelling indicates that in non-high-risk patients, cost savings with GreenLight XPS compared with TURP are determined by the proportion of procedures done as day cases. NICE's resource impact report estimates that the annual cost saving for the NHS in England is around £2.3 million. In a plausible scenario of 70% of treatments being done as day cases, the cost saving may be up to £3.2 million. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provides bespoke analysis to support NHS clients in their development of medical innovation for the benefit of their patients, local healthcare setting and/or the wider healthcare system. Examples of bespoke DAUK analysis provided to NHS clients are as follows; I. Facial palsy; Device Access worked with clinicians at East Grinstead Hospital who are developing a new rehabilitation system for patients with facial palsy as well as looking at how this illness can be prevented. To support this work, Device Access supplied analysis relating to the prevalence of Facial palsy and lymes disease. This analysis allowed the clinicians and the facial palsy charity to raise National awareness of the condition and look at developing new rehabilitation therapies across the country, as East Grinstead is currently the only NHS centre offering help for these patients. This new technology is being developed by the team at East Grinstead. https://www.youtube.com/watch?v=3MA_F7nIxEY&feature=youtu.be II. Gastro oesophageal reflux disease (GORD) research – understanding 4 Year patient outcomes from Antireflux fundoplication procedures; Device Access are currently working with Epsom and St Helier NHS Trust and supporting a paper to explain the impacts of the current standard of care for gastro oesophageal reflux disease surgery. The standard of care for these patients from a surgical perspective is a laparoscopic treatment called nissen fundoplication. DAUK HES analysis has shown that approximately 10% of these treatments result in further revision surgery. This research paper will be looking at those patients requiring revision surgery and those not in the relevant control group. Looking at risk factors impacting on the condition along with the cost and resource used to treat this group of patients. This will result in a new way of managing these patients and improving patient outcomes through the introduction of an alternative treatment through raising awareness of the current outcomes. This work was commissioned by the hospital but is being funded by a Medical Technology company who are developing a new therapy for the treatment of GORD. III. NHS Improvement; NHS Improvement recently approached Device Access for national information on the cause and incidence of pressure ulcers across NHS England. They wanted to understand what medical device technology is currently available and what impact these devices could have on the cost of pressure ulcers and patient care. 10% of all pressure ulcers that are treated in hospitals are community acquired with the remaining being acquired in hospital. The bespoke analysis completed by DAUK has provided NHS Improvement with an understanding of the most common reasons for admission leading to pressure ulcers. This analysis has been provided to NHS improvement at no cost. http://nhs.stopthepressure.co.uk 5. Department for International Trade DIT and Office for Life Sciences (OLS): This free service of providing the OLS and DIT with expertise around the NHS Market size has over the years encouraged several overseas companies to inwardly invest in the United Kingdom Market. This helps keep the UK as one of the world’s best places for inward investment, the NHS ahead in research, evaluation and adoption of innovative medical technologies, allowing patients to benefit from new innovative treatments sooner.
DEVICE ACCESS UK LTD	DEVICE ACCESS UK LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Overview of objective for processing: Device Access Limited conduct bespoke analysis on behalf of commercial companies, the NHS, academics and charities. This is a programme of work which identifies where medical and diagnostic technological devices can best be used by NHS providers in NHS patient care pathways to improve patient outcomes and to reduce lengths of stay, elective waiting times and diagnostic waiting times. This programme of work can be split into five distinct project areas as detailed below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd (DAUK) act as a bridge between medical technological device companies and the NHS by providing these companies with expertise and bespoke analysis in order to complete the NICE (National Institute of Clinical Excellence) process required before new innovations can be recommended and then adopted into the NHS for the benefit of patients. This activity is commissioned by the Medical Technology Companies, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. The device companies have no influence over the way in which the analysis is processed or the results. NICE programmes relating to the review of new or existing Medical and diagnostic technologies require a submission based on a detailed understanding of and real evidence around, current NHS patient pathways. NICE submissions offer relatively speedy assessments of selected innovations, with the aim of making relevant information available quickly to clinicians and NHS hospital decision makers, to support rapid adoption where this is appropriate. Submissions to NICE require the following which DAUK extract from HES datasets; • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and / or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Detailed understanding of cost-effectiveness improvements compared to current NHS pathways Before accepting a request from a commercial company and prior to any analysis or extraction of data, DAUK ensures that the technology meets the criteria of a potentially successful NICE submission by conducting internal research around the following formula (PICO); I. Population - Who would the technology treat II. Indication – how the technology works III. Comparator –How care is currently delivered to the population IV. Outcome – Are the benefits in line with the health and social care act 2014 Only once DAUK are satisfied that the technology meets these criteria and will be of benefit to patients and/or the health care system will they then conduct the bespoke analysis required for NICE submissions. As an example of this work, DAUK are currently providing bespoke analysis for a medical device company who have developed a non-invasive diagnostic technology which enables paramedics to diagnose stroke patients, and ensure they are sent to the relevant centres depending on the type of stroke. Currently in stroke diagnosis, paramedics are unable to determine whether the patient has had a minor stroke, which can be treated with thrombolysis drug therapy, or a major stroke or large vessel occlusion or LVO. LVO often requires a treatment called thrombectomy, which uses catheters to physically remove the clot from the brain which can only be performed in one of 27 specialist regional neuro centres. Recent research has shown a 73% reduction in disability over current therapy for the 8,000 patients diagnosed with large vessel occlusion every year. However, the most critical thing is that patients are triaged rapidly to these specialist centres and not an A&E in a District General Hospital who cannot perform the thrombectomy procedure. Delays not getting the right stroke intervention greatly affect patient outcomes. Through analysing HES with the Major Stroke centre network, DAUK are examining current stroke patient pathways, to understand how this technology will potentially impact and benefit patients and the NHS. The technology has the potential to improve patient outcomes for 44,000 patients a year in the UK that suffer from a stroke. DAUKs work will be used as part of the information required by the NICE submission process for this technology. 2. NIHR research applications on behalf of medical technological device companies: The objectives for processing the HES data are to provide bespoke analysis to medical technological device companies for their applications for research studies through the NIHR (National Institute for Health Research) . NIHR research is often required to provide clinical evidence for NICE applications. This activity is commissioned and funded by the Medical Technology Company, but can be funded directly to the Medical Technology Company though NIHR grants. The bespoke analysis provided by DAUK includes; • Clinical need analysis, evidence gap analysis and clinical endpoints • Understanding of the burden of disease; and how the current NHS patient population is diagnosed and or treated • The way the potential new technology works differently for this specific population in NHS England • Potential benefits in terms of improved outcomes, productivity gain, and care pathway services through the adoption of the proposed new technology • Understanding of potential cost-effectiveness improvements compared to current NHS pathways Using the Stroke diagnostic tool as the example again, DAUK have provided the bespoke analysis which has secured the NIHR research. DAUK have also identified the areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd provide expertise and bespoke analysis to medical technological device companies and the NHS in order to assist with adoption and diffusion of NICE recommended technologies so new innovations can be adopted into the NHS for the benefit of patients, and the healthcare system. This analysis is used to support local NHS adoption by providing information relating to benefits for patients in a local area. This information is sent by Device Access to both the commissioning medical device company and also the NHS hospital stake holders to demonstrate the benefit to comply with NICE guidance. This activity is commissioned and paid for by the by the Medical Technology Company. DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be used. The device companies have no influence over the way in which the analysis is processed or the results. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provide expertise and bespoke analysis to the NHS in order to assist with identifying new medical technology and diagnostics in support of relevant disease states with a view that these will be adopted into the NHS for the benefit of patients, and NHS hospitals. This activity is commissioned by the NHS, however, DAUK decide the manner and the process by which the analysis is carried out and how the outputs will be presented. This work does not include broad based economic consultancy services such as benchmarking but is limited to the purposes described in this document. DAUK would submit an amendment application to NHS Digital if DAUK were to consider a consultancy service which fell outside this agreed purpose. 5. Department for International Trade DIT and Office for Life Sciences: Since 2010, Device Access UK Ltd has supported the Department of International Trade or DIT, formerly known as the UK Trade and Investment (UKTI) who are part of the Government’s Office for Life Sciences (OLS). Part of the DIT and OLS objectives are to attract inward investment into the UK from companies across the world, to develop their business here in the life science sectors and DAUK has worked to support the strategy of the OLS and DIT. OLS and DIT commission DAUK with requests for market information, and DAUK provides free expertise on disease state, procedure volume and reimbursement. DAUK decide the manner and the process by which the analysis is carried out. DIT and OLS have no influence over the way in which the analysis is processed or the results.	Only substantive employees of Device Access UK Ltd have access to the data and only for the purposes described in this document. The data is not linked to any other data source or supplied to any third party. Processing occurs on a client by client basis and is bespoke to each client. Clients do not have access to any data via a portal. Processing activities are essentially the same for each project within the programme; a. DAUK use the HES data to perform the PICO – the analysis to check that the technology meets the criteria described and will be of benefit to patients and/or the healthcare system. b. DAUK analyse the HES data using industry standard algorithms and extensive expertise to extract the relevant cohort or population c. The analysis is used to produced bespoke reports and graphical charts which contain only aggregate data with small numbers suppressed in line with the HES Analysis guide d. Device Access produces HES information on disease state, procedure volume and reimbursement analysis. This analysis is at hospital or provider level, to ensure regional differences and variances in care pathways are analysed. However for project 5 this will only be at a national level.	Commercial companies specifically agree not to use the analysis provided by DAUK within general marketing, or within collateral material used by sales and marketing teams. These include sales brochures, emails, direct or mass mailing and advertising of medical technologies. Device Access Ltd do not allow portal access to the data by clients. Description of outputs have been provided below; 1. NICE applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions to NICE for evaluation. These bespoke outputs come in the form of reports and graphical charts and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NICE applications. Below are examples of DAUK analysis which has been used in NICE applications and have resulted in positive NICE guidance in the last 12 months; NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis NICE IPG549 Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death. NICE IPG578 Minimally invasive sacroiliac joint fusion surgery for chronic sacroiliac pain Prior to 2016, Device Access UK Ltd supported 10 additional NICE Medical Technology approvals leading to guidance; NICE MT 241 Device for relief of benign prostate hyperplasia NICE MIB 47 Diagnosis Dry Eye NICE TA 628 Intraoperative Radiotherapy for Breast Cancer NICE MIB 8 Airsonett temperature-controlled laminar airflow device for persistent allergic asthma NICE IPG 479 Device to manage ascities NICE IP 475 Device for relief of benign prostate hyperplasia NICE DG 12 Diagnostic Test To Manage Asthma NICE IPG 431 Laparoscopic insertion of a magnetic bead band for gastro-oesophageal reflux disease NICE IPG 396 Trabecular stent bypass microsurgery for open angle glaucoma 2. NIHR research applications on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with their submissions for NIHR funding. All bespoke outputs come in the form of reports and graphical charts and are aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used in evidence to support NIHR applications. 3. NHS adoption support on behalf of medical technological device companies: Device Access UK Ltd uses the HES analysis to support their Medical Technology clients with adoption of NICE approved technologies into the NHS. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is then used as evidence to support discussions in NHS Hospitals with key NHS stakeholders including finance directors, clinical managers, clinicians and CCG’s. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd uses the HES analysis to support their NHS clients with the development of new medical technology and diagnostics in support of relevant disease states. These bespoke outputs come in the form of reports, graphical charts and cost models and are always aggregated with small numbers suppressed in line with the HES analysis guide. This analysis is used as evidence to support medical technology developments in NHS Hospitals with key NHS stakeholders including commissioners, clinicians and researchers 5. Department for International Trade DIT and Office for Life Sciences (OLS): A basic summary of National HES extracts of disease state, procedure volume and reimbursement analysis is supplied free of charge by DAUK to the DIT and OLS for use in direct and combined discussions with potential overseas companies considering UK market entry. DAUK only provides DIT and OLS with National information, not by hospital or provider. Outputs are therefore aggregated, with small numbers suppressed in line with the HES Analysis Guide.	1. NICE applications on behalf of medical technological device companies: The transformation programme of the NHS recognises the importance of supporting innovation and enabling the fast adoption of cost-effective new technologies to secure improved patient outcomes. Device Access UK Ltd are recognised by medical device companies and NICE as experts in the use of HES data to provide robust and trusted analysis which allows the NHS to assess the impact of medical innovation on patients and the health care system. Without the work DAUK provide, innovation could be delayed in the NICE process and this would have a negative impact on the time it would take for the benefits of such technologies to reach patients and the NHS. Two examples of the benefits to the health and social care derived from the data supplied by Device Access UK Ltd and NICE approvals are; I. Benign prostate hyperplasia (BPH). Current NHS Treatments result in an over 2-day length of stay, resulting in over 45,000 bed days. Through data analysis, Device Access helped identify an opportunity for these procedures to be performed on a day case basis supporting best practice, benefiting the patient and making cost savings for the NHS. View the NICE news release here https://www.nice.org.uk/news/article/thousands-of-men-with-enlarged-prostates-could-be-helped-by-new-nice-guidance-on-laser-device II. Normothermic extracorporeal preservation of hearts for transplantation following donation after brainstem death http://www.bbc.co.uk/news/health-32056350 2. NIHR research applications on behalf of medical technological device companies: NICE often advise medical device companies that further clinical research would be required as part of the NICE submission process. DAUK provide accurate and trusted bespoke analysis for the medical technological device companies to complete the NIHR evaluation. As with objective 1, DAUK’s work means that NIHR can assess sound analysis helping them evaluate quickly whether further research would be worthwhile. The DAUK analysis also identifies areas of the country where there is most need thus enabling faster patient enrolment into clinical trials. It ensures potential evidence gaps are eliminated and through research with the NIHR, help the NHS determine how to roll out innovations for the benefit of patients. 3. NHS adoption support on behalf of medical technological device companies: The NHS is a highly complex and fragmented system. The infrastructure to rapidly and efficiently spread medical innovations, in a way that ensures that every patient in the NHS benefits no matter which hospital or which GP practice they visit, does not yet exist. The work of DAUK contributes to the process of helping these innovations reach and get adopted by the NHS in a more efficient way for the benefit of patients and the healthcare system. Below are 2 examples of NICE approved technologies Device Access is supporting following NICE evaluation within the last 12 months. In the examples, Device Access have provided individual Hospitals directly with information relating to length of stay and bespoke analysis around the cost benefit for adoption which has assisted decision makers in NHS hospitals to commission new care pathways to provide a day case setting for these patients suffering from benign prostate hyperplasia and chronic sinusitis. This has resulted in faster NHS uptake for the technology and clear benefits for these patients. i. NICE MTG30 XprESS multi sinus dilation system for treating chronic sinusitis- XprESS multi sinus dilation system for treating uncomplicated chronic sinusitis after medical treatment has failed is supported by NICE. Treatment with XprESS leads to a rapid and sustained improvement for the patient in chronic symptoms, fewer acute episodes and improved quality of life which is comparable to functional endoscopic sinus surgery (FESS). This treatment is of further benefit to the patient by having faster post treatment recovery times, and can more often be done under local anaesthesia. Cost modelling indicates that XprESS is cost saving compared with FESS when treatment is done using local anaesthetic in an outpatient setting. By adopting this technology, the NHS in England may save around £7.4 million a year by 2020. Estimated savings are mainly achieved through the shift of treatment from operating theatre to outpatient setting. ii. NICE MTG 29 GreenLight XPS for treating benign prostatic hyperplasia- GreenLight XPS for treating benign prostatic hyperplasia is supported by NICE in non-high-risk patients. GreenLight XPS is at least as effective in these patients as transurethral resection of the prostate (TURP), but can more often be done as a day-case procedure and is far less invasive for the benefit of the patient. Cost modelling indicates that in non-high-risk patients, cost savings with GreenLight XPS compared with TURP are determined by the proportion of procedures done as day cases. NICE's resource impact report estimates that the annual cost saving for the NHS in England is around £2.3 million. In a plausible scenario of 70% of treatments being done as day cases, the cost saving may be up to £3.2 million. 4. NHS Consultancy with regards to medical and diagnostic devices for patient diseases: Device Access UK Ltd provides bespoke analysis to support NHS clients in their development of medical innovation for the benefit of their patients, local healthcare setting and/or the wider healthcare system. Examples of bespoke DAUK analysis provided to NHS clients are as follows; I. Facial palsy; Device Access worked with clinicians at East Grinstead Hospital who are developing a new rehabilitation system for patients with facial palsy as well as looking at how this illness can be prevented. To support this work, Device Access supplied analysis relating to the prevalence of Facial palsy and lymes disease. This analysis allowed the clinicians and the facial palsy charity to raise National awareness of the condition and look at developing new rehabilitation therapies across the country, as East Grinstead is currently the only NHS centre offering help for these patients. This new technology is being developed by the team at East Grinstead. https://www.youtube.com/watch?v=3MA_F7nIxEY&feature=youtu.be II. Gastro oesophageal reflux disease (GORD) research – understanding 4 Year patient outcomes from Antireflux fundoplication procedures; Device Access are currently working with Epsom and St Helier NHS Trust and supporting a paper to explain the impacts of the current standard of care for gastro oesophageal reflux disease surgery. The standard of care for these patients from a surgical perspective is a laparoscopic treatment called nissen fundoplication. DAUK HES analysis has shown that approximately 10% of these treatments result in further revision surgery. This research paper will be looking at those patients requiring revision surgery and those not in the relevant control group. Looking at risk factors impacting on the condition along with the cost and resource used to treat this group of patients. This will result in a new way of managing these patients and improving patient outcomes through the introduction of an alternative treatment through raising awareness of the current outcomes. This work was commissioned by the hospital but is being funded by a Medical Technology company who are developing a new therapy for the treatment of GORD. III. NHS Improvement; NHS Improvement recently approached Device Access for national information on the cause and incidence of pressure ulcers across NHS England. They wanted to understand what medical device technology is currently available and what impact these devices could have on the cost of pressure ulcers and patient care. 10% of all pressure ulcers that are treated in hospitals are community acquired with the remaining being acquired in hospital. The bespoke analysis completed by DAUK has provided NHS Improvement with an understanding of the most common reasons for admission leading to pressure ulcers. This analysis has been provided to NHS improvement at no cost. http://nhs.stopthepressure.co.uk 5. Department for International Trade DIT and Office for Life Sciences (OLS): This free service of providing the OLS and DIT with expertise around the NHS Market size has over the years encouraged several overseas companies to inwardly invest in the United Kingdom Market. This helps keep the UK as one of the world’s best places for inward investment, the NHS ahead in research, evaluation and adoption of innovative medical technologies, allowing patients to benefit from new innovative treatments sooner.
DR. FOSTER LIMITED	DR. FOSTER LIMITED	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	Ongoing	N	The Summary Hospital-level Mortality Indicator (SHMI) reports on mortality at trust level across the NHS in England using a standard and transparent methodology. It is produced and published quarterly as a National Statistic by NHS Digital. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. Dr Foster provides NHS customers with a free of charge dashboard that allows for them to analyse and benchmark their performance in terms of the SHMI measure of mortality. The SHMI data is required to produce/analyse statistics using births/deaths data solely to help the NHS perform its duties. Any analysis produced using SHMI data will not be made available to non-NHS organisations.	Landing On landing the SHMI dataset will be recorded on the Dr Foster Data Asset Register (DAR) and allocated a unique Asset Tag, in addition a Date of Destruction will be recorded along with Acknowledgements required in the publication of these data. Processing NOTE: Data will flow to Dr Foster Ltd only. Data will be accessed by the named users within this agreement, i.e. substantive employees of Dr Foster Limited. Once logged in the Data Asset Register, it is handed over to a named individual who will load these data onto a secure central processing server located at Dorset Rise, a ‘SHMI’ Extract, Transform & Load process (documented) will then be run to transform record level data and then appended into a aggregated SQL database (aggregated at Provider & Diagnosis group level). Once processed the data will then be quality checked and upon completion published to the live client facing Dr Foster Dashboard Tool. Publication SHMI data, which has been available to Dr Foster since 2011, will only be made available to NHS Trusts via the Dr Foster Dashboard Tool. Note: This tool is provided Free of Charge to all NHS Trusts Destruction Raw SHMI data will be Blancco (CESG approved) file shredded with certificated evidence when Date of Destruction is applicable (identified on Dr Foster’s Data Asset Register via a monthly process). Telecity is listed as a storage address for recovery purposes only. Telecity do not have access to the Dr Foster Ltd. Server or Server passwords and will not be accessing/backing up the data provided within this agreement. NHS Digital require Dr Foster Ltd to maintain a list of the organisations who have requested the work to be carried out with SHMI data, together with confirmation that a Contract is in place with each Trust. The commissioning organisation must also confirm to Dr Foster that they have: • Instructed the applicant that no additional use can be made of the data • Instructed the commissioned organisation that the data supplied must be securely destroyed after use The S42(4) body understands that they also remain responsible for any unauthorised use or disclosure of the data supplied to those working on their behalf. The information needs to be made available to NHS Digital upon request. Any data provided will only be used by Dr Foster for the purposes, activities, and outputs defined in this agreement. The ONS Terms and Conditions will be adhered to with regards to the data being supplied.	The Dr Foster Dashboard Tool Online application is available to NHS Acute Trusts, only which compares the two leading mortality indicators in England – the SHMI and the Dr Foster Hospital Standard Mortality Ratio (HSMR). All data is aggregated with small numbers suppressed in line with the HES analysis guide and there are no links to any identifiers. It enables users to uncover and investigate some of the potential root causes of differences between these indicators and to investigate variations against peers. SHMI (and HSMR) is only available to NHS customers within England and Wales. No organisation or individual(s) outside the NHS will have access to the SHMI and HSMR tools. Key outputs: • Overview of SHMI and Hospital Standard Mortality Ratio (HSMR) - summary charts, trends and breakdowns. • Graphical Dashboard comparing mortality measures side-by-side. • Analyse national position, regional comparisons and custom peer groups. • The ability to drill down and investigate by SHMI supergroup, Clinical Classification System (CCS) group or user-defined basket of diagnoses. Typical end users • Chief Executives • Medical Directors • NHS Managers • Information Analysts • Clinicians • Nurses	Dr Foster provides all of its NHS customers free of charge with a tool to allow them to monitor and improve the quality and safety of care they provide by comparing the two leading mortality indicators in England: SHMI and HSMR. It enables their customers to perform a root cause analysis of their SHMI, in line with the requirements of the NHS Operating Framework. “All hospital trusts, regardless of whether they are outliers, need to examine, understand and explain their SHMI and identify and act where performance is falling short. Should a trust be an outlier on any mortality measure it should scrutinise the underlying data to understand the reason and take appropriate action.” The Operating Framework for the NHS in England 2012/13 As identified in the commissioning letter, with SHMI data provided, Dr Foster have been able to help their customers: - uncover and investigate some of the potential root causes of differences between the various mortality indicators and to investigate variations against peers. - Understand any variation between SHMI and HSMR at a summary level and what drives that variation. - Gain the insight they need to embed SHMI within their mortality management programme, alongside other mortality indicators, such as HSMR and Deaths after Surgery. - investigate and understand the impact of the inclusion of post-discharge mortality data (only available through the SHMI mortality indicator). - The ability to drill down and investigate by SHMI supergroup, CCS group or user-defined basket of diagnoses. Expected measurable benefits include: • Enable customers to measure, compare and benchmark mortality and alerting those who have higher than expected mortality levels to encourage efforts to investigate and address these. Dr Foster's independent position is beneficial as it supports customer focus on information and data as opposed to anecdotal evidence. • Identify mortality trends across hospitals. • Instigate clinical audit and inform investigations related to quality of care, such as highlighting poor clinical coding or quality/efficiency concerns. • Validate other mortality indicators – such as HSMR and crude mortality. • Understand and quickly visualise SHMI & HSMR indicators side by side. How will these be measured: • By their nature, Dr Foster analytical tools allow the performance of customers to be monitored and trended over time. It can indicate changes to quality and efficiency performance particularly in instances where trusts have been alerted and Dr Foster has worked with them to understand the causes of worse than expected performance. When will these be achieved: • It is not possible to outline a specific target date for achievement of the benefits outlined above as they are reliant on a range of factors outside of Dr Foster immediate control. However, whenever there are areas of particular concern about performance against key indicators, Dr Foster acts immediately to make their customers aware and offer assistance in better understanding and addressing them. • In addition benefits are ongoing as these outputs are used within NHS Trusts internal monthly reporting and quality processes.
ERNST AND YOUNG LLP	ERNST AND YOUNG LLP	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Ernst and Young LLP (EY) work with a number of providers and funders of NHS care across the NHS spread across England, Wales and Scotland along with national bodies as listed https://www.gov.uk/government/publications/arms-lengthbodies/ our-arms-length-bodies . In addition, EY works with international healthcare organisations (this does not include device or pharmaceutical companies or health insurers). The work carried out for both types of clients is aimed at optimising performance, and having access to detailed information (e.g. benchmarking relating to NHS Trusts) is key to this. EY use the data to calculate relevant local and national Key Performance Indicators to share with clients and to bring about change within their clients. EY's request for these SUS PbR data sets is so that EY can quickly, and with insight, be responsive to tenders from the whole health and social care community and economy. More information can be found at http://www.ey.com/UK/en/Industries/Government---Public-Sector/Healthcare Around 50% of tenders the EY health team responded to last year for the UK&I business were contracted under the Consultancy ONE framework. This is a framework to which EY has been appointed by the Cabinet Office to be able to tender for services. There are no more than 20 suppliers nationally for each lot and EY has had to undergo a rigorous process of vetting by the Cabinet Office / Government Procurement Services to be eligible to respond to tenders released under this framework. Of the remainder, some are contracted under smaller frameworks such as gCloud, or via locally tendered/uncontested work outside a framework and thus contracted directly with the healthcare organisation. The tender mechanism does not differ depending on the type of service contracted. EY work on a wide variety of projects under these frameworks and all are slightly different in nature, owing to the needs of the NHS tendering, however, the majority of which fall into 5 categories: 1. Performance improvement: Assisting organisations in improvements in cost, outcomes and clinical pathways a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations in finding opportunities to improve from a cost, clinical pathway or outcome perspective b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 2. Integration and Restructuring 􀍴􀀃Assisting organisations who are planning to merge or partner, and working with providers who are close to or entering the failure regime. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using aggregate benchmark data in order to assist organisations to identify opportunities to merge or partner and to assist organisations to navigate the failure regime and also assisting organisations in understanding what a new merged organisation performance could look like. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 3. Local Health Economy Transformation - Understanding of capacity and demand and financial balance across a whole system or health economy a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations to better understand the demand across the whole system or health economy b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 4. Economics and Pricing. Working with national bodies such as NHS Improvement and NHS England on understanding the impact of local and national pricing decisions. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using national data to quantify the impact and understand the effects of policy decisions such as understanding the impact or adequacy of top-up payments. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 5. Worldwide Benchmarking To provide international benchmarks in areas such as Length of Stay and gross volume data to NHS organisations working overseas, for UK national government bodies such as UKTI, for clients near to UK such as in the Channel Islands, Ireland and for wider international comparison. We are also starting to work with healthcare providers in other countries who interested in their international performance. a. Aggregated benchmarking data will be provided to these organisations with the understanding that they consent to their data being used to benchmark against NHS clients. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients. c. EY has actively invested in developing its international capability. This has been focussed on working with NHS organisations, which includes Trusts, Academic Health Science Networks, and HealthcareUK, to support them to take their training, education and clinical operation capabilities to new markets. This is part of a wider UK PLC and public sector push, as demonstrated by the role of HealthcareUK, jointly sponsored by the DoH and NHSE. These clients make up around 10% of EY's revenue base at the moment. The intention is to provide aggregate (e.g. ICD/OPCS and POD level (or similar) benchmarks to these clients to help these providers or commissioners to improve their performance. This will benefit the NHS by having international comparators in return to understand international best practice. d. When working internationally with NHS organisations there have been frequent questions around how NHS performance compares with that of the host country. NHS Digital data will support that benchmarking, which in turn can support with the development of (i) feasibility studies (in collaboration with OECD, WHO and European monitoring systems), (ii) operational models, (iii) development of new healthcare facilities. In turn these will all support increased revenues to the NHS organisations, alongside options for organisations to support their education and research agenda and the reputation of the NHS and EY globally. e. The processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. The client base of all UK&I EY Advisory (as at January 2017) is split as follows: 31 Acute providers 3 CSUs 14 CCGs 17 Mental Health and/or Community providers 1 Ambulance Trust Regarding category 5) Worldwide Healthcare clients, our client list as at January 2017: 2 NHS organisations working overseas 5 Canadian Healthcare clients HealthcareUK / UK Trade and Investment (UKTI) Client in the Channel Islands (Publicly funded hospital) 10 US clients EY use the data in a variety of ways on these projects. For example EY would use it for basic benchmarking on Performance Improvement and for an Integration project EY would use the first 4 digits of postcodes to look at the site where fewest people would have to travel to attend. This is dependent on the engagement agreements EY have in place for each of these pieces of work. EY share results in aggregate form only. All outputs will have small numbers suppressed and will follow the HES Analysis Guide. EY do not share raw data. For the overseas clients, the processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. Data will not be used within EY for proactive targeting of prospective clients, but will for the fulfilling of client requirements as stated within this purpose; including responding to tenders for service and in external thought leadership production. External thought leadership consists of reports written to encourage thoughtful discussion and that are published for an audience of interested parties. An example being understanding the levels of specialist activity in non-specialist trust. Only aggregate data will be used and copyright for the data will be attributed to NHS Digital.	Data will be obtained from NHS Digital in a pseudonymised form and uploaded to a secure environment. From here the data will be manipulated to be integrated into the EY Health Analytics Data Platform, where it can be accessed by the end users in alignment with the small numbers policy. The NHS Digital data will not be linked with any other personal data. EY and Rackspace are both ISO 27001 compliant. The derived data will always be aggregated. Patient level data will not be transferred off the servers. All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. No data will be linked to record patient level data, and record-level data will not be removed from the secure servers. There will be two types of users: - Standard users are EY staff and will only have access to aggregated data (such as HRG level benchmarks) with small numbers suppressed and be able to change the view of such data that to be most useful to the client for purposes outlined earlier in section 5; - Super-users (also EY staff) and able to access raw patient level data. The named users will be limited (up to a maximum of 20) and will access the data set remotely via a secure, encrypted channel. Authorisation controls will be in place to ensure that named users have permissions which restrict them to access only the data designated for their access. This is ensured using role based permissions set up on the EY Active Directory server, a log and audit trail of access and data downloads is maintained and regularly monitored. Only data aggregated in line with the HES analysis guide may be downloaded. Data will not leave the EEA unless aggregated and in line with the HES analysis guide. Benchmarks • National benchmarks, for example day case rates or mortality rates, will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software for communication to EY colleagues and clients. Access by Superusers • Superusers must access the data from the UK. • Superusers of the analysis are EY employees only, accessing at the addresses stated for processing – giving access to the patient level data to any other group would be subject to a further application to DARS (and only given once an approval had been received). • Access to the patient level data by super users will be via an encrypted secure remote access channel, allowing only those with the agreed credentials to view the toolsets and applications within the Health analytics database. • The data will always remain resident within the data centre and will be manipulated remotely via Virtual Desktop Interface (VDI) protocol. This is particularly important in relation to users of the data for purpose 5, as it ensures that no data leaves the UK and that the data is observed through a window and manipulated on the UK based database server. • All superusers are EY UK Staff. Access by standard users • Standard users will only be able to access aggregated data will small numbers suppressed in line with advice from the HES analysis guide. • EY clients will have access to the aggregated outputs of analysis including benchmarks and visualisations. No patient level data will be available to clients. Other standard users will have access to aggregated benchmarks with small number suppression. Data Security Panel • EY will host an internal data security panel to review all requests for use of the national data. This panel will comprise a senior team to include QA and information governance leads, legal representative, and senior superusers. Where there are outstanding questions for non-standard requests, the panel will defer to NHS Digital for a decision. We have attached draft terms of reference for this panel for reference purposes. Further Security Information • EY have purchased a private space in the Rackspace cloud. This gives EY control over the locations where the data will be resident. • Cybersecurity protocols – Rackspace have agreed to additional third party security applications over and above their normal technical and operational security controls. This includes EY-managed encryption at rest, vulnerability scanning, privilege management and others. Applications and data are backed up using a dedicated Managed Backup facility at the Rackspace LON3 data centre in Slough. • All EY staff are subject to the global client confidentiality policy which outlines every employee’s responsibility with regards confidential information.	EY outputs are bespoke to each client and each engagement has their own milestones and delivery dates. These are ongoing. Client requested data will be transferred by EY employees to Excel or other visualisation software such as Spotfire or PowerPoint for communication to colleagues and clients. The outputs will be aggregated with small numbers suppressed. Patient level data will not be transferred off the servers. All outputs will follow the HES analysis guide. No data will be linked to record patient level data. All data extracts will be quality assured by a senior member of the EY team before being used to deliver the scope of work agreed with the client. The following outputs may apply depending upon the individual service requested. - Benchmarking applies across all services. National benchmarks will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software including EY’s health platform for communication to EY colleagues and clients. - The derived data will always be aggregated. - Outputs will be available as per the scope of services and engagement letter but is usually the Board (including non-executive members) and service managers/clinical directors. 1. Performance Optimisation - Reports – A summary of outputs outlined below which may be made available to third parties such as regulators (NHS Improvement, TDA etc) - Benchmarking – e.g.. Showing an organisations position against selected peer group or national average for DNA rates - Drive Time Analysis – e.g. heat maps to show where patients are travelling from to access services to understand whether outreach clinics would be more accessible to patients - Performance Optimisation Dashboards – Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking. 2. Integration and Restructuring - Reports – EY may be asked by a regulator or organisation public sector to form a judgement on the future sustainability of their organisation and the options available if it is deemed not viable in the current form. - Benchmarking – If two or more organisations are merging then it’s useful for them to have an understanding of their relative performance to each other which would be derived from local data but also to a new group of peers for a potential combined organisation to enable the boards to understand how they would compare. - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction pre and/or post merger - Local Health Economy Plan – If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 3. Local Health Economy Transformation - Reports – EY are asked to size the financial gap in a health economy and then provide a view on how to close the gap, some of this can be through understanding differences in activity and efficiency for different providers in the patch. - Benchmarking – Aggregated benchmarking for commissioners and providers (at HRG/POD level) allows the identification of different pathways of care and health inequalities amongst the local population - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction - Local Health Economy Plan - If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains which are often similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 4. Economics and Pricing - Reports – An example of the type of report EY are asked to compile is using PLICs or reference costs for providers and examining the margins associated with particular HRGs or specialties, in instances such as this EY would be using NHS Digital data to identify peers using a co-morbidity coefficient or similar. - Benchmarking – Linked to the point above, EY would be using NHS Digital data to identify peers and possible reasons for cost drivers such as average bed days, demographics etc. - Size Impact of tariff change to local and national NHS organisations – Where a change in the tariff, such as the application of a top up tariff or an agreement of block funding is indicated based on a review of PLICs data then the HRG volume information would be used to estimate future possible cost to commissioners and income for the provider. This can be used to develop an evidence based case for the commissioner. 5. Worldwide Benchmarking From EY’s UK&I International Unit EY focus on working with NHS and other publicly funded organisations to: - Develop business cases and ‘go-to-market’ models for services - Develop pricing responses, investment requirements, effective financial risk mechanisms - Work with international private and public providers of healthcare to assist them in understanding their operational performance efficiency	As above, the lifecycle of EY engagements are such that at any one time EY are in scoping, design, delivery and sustainability phases across a number of projects in the country. The nature of EY’s work is to help providers and commissioners identify areas of poor performance or poor efficiency and work with them to improve. Some of EY’s projects are subject to tight confidentiality agreements and the scope/client is not known to that outside of the immediate engagement team and therefore EY cannot disclose this to others. These activities are essential to the future of the NHS – without efficiency use of NHS resources patient care will suffer and waiting lists grow. It is important that EY are able to provide EY’s clients with relevant data around the performance of other NHS trusts so that suitable benchmarks and improvement targets can be identified. It is also important that data outlining flows of patients around the NHS are available to EY’s clients to help them understand what services they need to provide and where. This information is reliant on a national data set but it is not reliant on the provision of patient level data to EY’s clients. Therefore EY need access to the full PbR dataset in-house, but EY clients and the wider project teams need only to work with the derivative data and clients will not receive patient-level data. Generally EY observe benefit realisations in the following areas (Subject to terms and scope of contract): 1. Performance Optimisation - Cost efficiencies to enable financial stability - Improve quality and patient experience - Meeting access targets 2. Integration and Restructuring - Improvements to clinical models - Compliance with Treasury Green Book - Cost efficiencies to enable financial stability - Improve quality and patient experience 3. Local Health Economy Transformation - Recognise achievements against national targets - Scenario analysis to identify efficiency improvements - Pathway reconfiguration - Commissioner Intentions setting 4. Economics and Pricing - Identify eligibilities for top up funding - Financial stability through coding due diligence - Activity plan development 5. Worldwide Benchmarking - Improvement in clinical productivity - Innovative international best practise benchmarking - Market analysis on a like-for-like basis internationally for key performance benchmarks - Non-NHS income generation for NHS organisations Commercial statement: This data will be used most commonly for EY analysis and understand the relative performance of organisations and health economies. The data will be used to support EY’s final work products but in most instances this will not be the sole purpose for which EY have been commissioned. If the data is used as part of a thought leadership piece, then the data source will be clearly referenced.
ERNST AND YOUNG LLP	ERNST AND YOUNG LLP	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Ernst and Young LLP (EY) work with a number of providers and funders of NHS care across the NHS spread across England, Wales and Scotland along with national bodies as listed https://www.gov.uk/government/publications/arms-lengthbodies/ our-arms-length-bodies . In addition, EY works with international healthcare organisations (this does not include device or pharmaceutical companies or health insurers). The work carried out for both types of clients is aimed at optimising performance, and having access to detailed information (e.g. benchmarking relating to NHS Trusts) is key to this. EY use the data to calculate relevant local and national Key Performance Indicators to share with clients and to bring about change within their clients. EY's request for these SUS PbR data sets is so that EY can quickly, and with insight, be responsive to tenders from the whole health and social care community and economy. More information can be found at http://www.ey.com/UK/en/Industries/Government---Public-Sector/Healthcare Around 50% of tenders the EY health team responded to last year for the UK&I business were contracted under the Consultancy ONE framework. This is a framework to which EY has been appointed by the Cabinet Office to be able to tender for services. There are no more than 20 suppliers nationally for each lot and EY has had to undergo a rigorous process of vetting by the Cabinet Office / Government Procurement Services to be eligible to respond to tenders released under this framework. Of the remainder, some are contracted under smaller frameworks such as gCloud, or via locally tendered/uncontested work outside a framework and thus contracted directly with the healthcare organisation. The tender mechanism does not differ depending on the type of service contracted. EY work on a wide variety of projects under these frameworks and all are slightly different in nature, owing to the needs of the NHS tendering, however, the majority of which fall into 5 categories: 1. Performance improvement: Assisting organisations in improvements in cost, outcomes and clinical pathways a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations in finding opportunities to improve from a cost, clinical pathway or outcome perspective b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 2. Integration and Restructuring 􀍴􀀃Assisting organisations who are planning to merge or partner, and working with providers who are close to or entering the failure regime. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using aggregate benchmark data in order to assist organisations to identify opportunities to merge or partner and to assist organisations to navigate the failure regime and also assisting organisations in understanding what a new merged organisation performance could look like. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 3. Local Health Economy Transformation - Understanding of capacity and demand and financial balance across a whole system or health economy a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations to better understand the demand across the whole system or health economy b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 4. Economics and Pricing. Working with national bodies such as NHS Improvement and NHS England on understanding the impact of local and national pricing decisions. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using national data to quantify the impact and understand the effects of policy decisions such as understanding the impact or adequacy of top-up payments. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 5. Worldwide Benchmarking To provide international benchmarks in areas such as Length of Stay and gross volume data to NHS organisations working overseas, for UK national government bodies such as UKTI, for clients near to UK such as in the Channel Islands, Ireland and for wider international comparison. We are also starting to work with healthcare providers in other countries who interested in their international performance. a. Aggregated benchmarking data will be provided to these organisations with the understanding that they consent to their data being used to benchmark against NHS clients. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients. c. EY has actively invested in developing its international capability. This has been focussed on working with NHS organisations, which includes Trusts, Academic Health Science Networks, and HealthcareUK, to support them to take their training, education and clinical operation capabilities to new markets. This is part of a wider UK PLC and public sector push, as demonstrated by the role of HealthcareUK, jointly sponsored by the DoH and NHSE. These clients make up around 10% of EY's revenue base at the moment. The intention is to provide aggregate (e.g. ICD/OPCS and POD level (or similar) benchmarks to these clients to help these providers or commissioners to improve their performance. This will benefit the NHS by having international comparators in return to understand international best practice. d. When working internationally with NHS organisations there have been frequent questions around how NHS performance compares with that of the host country. NHS Digital data will support that benchmarking, which in turn can support with the development of (i) feasibility studies (in collaboration with OECD, WHO and European monitoring systems), (ii) operational models, (iii) development of new healthcare facilities. In turn these will all support increased revenues to the NHS organisations, alongside options for organisations to support their education and research agenda and the reputation of the NHS and EY globally. e. The processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. The client base of all UK&I EY Advisory (as at January 2017) is split as follows: 31 Acute providers 3 CSUs 14 CCGs 17 Mental Health and/or Community providers 1 Ambulance Trust Regarding category 5) Worldwide Healthcare clients, our client list as at January 2017: 2 NHS organisations working overseas 5 Canadian Healthcare clients HealthcareUK / UK Trade and Investment (UKTI) Client in the Channel Islands (Publicly funded hospital) 10 US clients EY use the data in a variety of ways on these projects. For example EY would use it for basic benchmarking on Performance Improvement and for an Integration project EY would use the first 4 digits of postcodes to look at the site where fewest people would have to travel to attend. This is dependent on the engagement agreements EY have in place for each of these pieces of work. EY share results in aggregate form only. All outputs will have small numbers suppressed and will follow the HES Analysis Guide. EY do not share raw data. For the overseas clients, the processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. Data will not be used within EY for proactive targeting of prospective clients, but will for the fulfilling of client requirements as stated within this purpose; including responding to tenders for service and in external thought leadership production. External thought leadership consists of reports written to encourage thoughtful discussion and that are published for an audience of interested parties. An example being understanding the levels of specialist activity in non-specialist trust. Only aggregate data will be used and copyright for the data will be attributed to NHS Digital.	Data will be obtained from NHS Digital in a pseudonymised form and uploaded to a secure environment. From here the data will be manipulated to be integrated into the EY Health Analytics Data Platform, where it can be accessed by the end users in alignment with the small numbers policy. The NHS Digital data will not be linked with any other personal data. EY and Rackspace are both ISO 27001 compliant. The derived data will always be aggregated. Patient level data will not be transferred off the servers. All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. No data will be linked to record patient level data, and record-level data will not be removed from the secure servers. There will be two types of users: - Standard users are EY staff and will only have access to aggregated data (such as HRG level benchmarks) with small numbers suppressed and be able to change the view of such data that to be most useful to the client for purposes outlined earlier in section 5; - Super-users (also EY staff) and able to access raw patient level data. The named users will be limited (up to a maximum of 20) and will access the data set remotely via a secure, encrypted channel. Authorisation controls will be in place to ensure that named users have permissions which restrict them to access only the data designated for their access. This is ensured using role based permissions set up on the EY Active Directory server, a log and audit trail of access and data downloads is maintained and regularly monitored. Only data aggregated in line with the HES analysis guide may be downloaded. Data will not leave the EEA unless aggregated and in line with the HES analysis guide. Benchmarks • National benchmarks, for example day case rates or mortality rates, will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software for communication to EY colleagues and clients. Access by Superusers • Superusers must access the data from the UK. • Superusers of the analysis are EY employees only, accessing at the addresses stated for processing – giving access to the patient level data to any other group would be subject to a further application to DARS (and only given once an approval had been received). • Access to the patient level data by super users will be via an encrypted secure remote access channel, allowing only those with the agreed credentials to view the toolsets and applications within the Health analytics database. • The data will always remain resident within the data centre and will be manipulated remotely via Virtual Desktop Interface (VDI) protocol. This is particularly important in relation to users of the data for purpose 5, as it ensures that no data leaves the UK and that the data is observed through a window and manipulated on the UK based database server. • All superusers are EY UK Staff. Access by standard users • Standard users will only be able to access aggregated data will small numbers suppressed in line with advice from the HES analysis guide. • EY clients will have access to the aggregated outputs of analysis including benchmarks and visualisations. No patient level data will be available to clients. Other standard users will have access to aggregated benchmarks with small number suppression. Data Security Panel • EY will host an internal data security panel to review all requests for use of the national data. This panel will comprise a senior team to include QA and information governance leads, legal representative, and senior superusers. Where there are outstanding questions for non-standard requests, the panel will defer to NHS Digital for a decision. We have attached draft terms of reference for this panel for reference purposes. Further Security Information • EY have purchased a private space in the Rackspace cloud. This gives EY control over the locations where the data will be resident. • Cybersecurity protocols – Rackspace have agreed to additional third party security applications over and above their normal technical and operational security controls. This includes EY-managed encryption at rest, vulnerability scanning, privilege management and others. Applications and data are backed up using a dedicated Managed Backup facility at the Rackspace LON3 data centre in Slough. • All EY staff are subject to the global client confidentiality policy which outlines every employee’s responsibility with regards confidential information.	EY outputs are bespoke to each client and each engagement has their own milestones and delivery dates. These are ongoing. Client requested data will be transferred by EY employees to Excel or other visualisation software such as Spotfire or PowerPoint for communication to colleagues and clients. The outputs will be aggregated with small numbers suppressed. Patient level data will not be transferred off the servers. All outputs will follow the HES analysis guide. No data will be linked to record patient level data. All data extracts will be quality assured by a senior member of the EY team before being used to deliver the scope of work agreed with the client. The following outputs may apply depending upon the individual service requested. - Benchmarking applies across all services. National benchmarks will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software including EY’s health platform for communication to EY colleagues and clients. - The derived data will always be aggregated. - Outputs will be available as per the scope of services and engagement letter but is usually the Board (including non-executive members) and service managers/clinical directors. 1. Performance Optimisation - Reports – A summary of outputs outlined below which may be made available to third parties such as regulators (NHS Improvement, TDA etc) - Benchmarking – e.g.. Showing an organisations position against selected peer group or national average for DNA rates - Drive Time Analysis – e.g. heat maps to show where patients are travelling from to access services to understand whether outreach clinics would be more accessible to patients - Performance Optimisation Dashboards – Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking. 2. Integration and Restructuring - Reports – EY may be asked by a regulator or organisation public sector to form a judgement on the future sustainability of their organisation and the options available if it is deemed not viable in the current form. - Benchmarking – If two or more organisations are merging then it’s useful for them to have an understanding of their relative performance to each other which would be derived from local data but also to a new group of peers for a potential combined organisation to enable the boards to understand how they would compare. - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction pre and/or post merger - Local Health Economy Plan – If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 3. Local Health Economy Transformation - Reports – EY are asked to size the financial gap in a health economy and then provide a view on how to close the gap, some of this can be through understanding differences in activity and efficiency for different providers in the patch. - Benchmarking – Aggregated benchmarking for commissioners and providers (at HRG/POD level) allows the identification of different pathways of care and health inequalities amongst the local population - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction - Local Health Economy Plan - If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains which are often similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 4. Economics and Pricing - Reports – An example of the type of report EY are asked to compile is using PLICs or reference costs for providers and examining the margins associated with particular HRGs or specialties, in instances such as this EY would be using NHS Digital data to identify peers using a co-morbidity coefficient or similar. - Benchmarking – Linked to the point above, EY would be using NHS Digital data to identify peers and possible reasons for cost drivers such as average bed days, demographics etc. - Size Impact of tariff change to local and national NHS organisations – Where a change in the tariff, such as the application of a top up tariff or an agreement of block funding is indicated based on a review of PLICs data then the HRG volume information would be used to estimate future possible cost to commissioners and income for the provider. This can be used to develop an evidence based case for the commissioner. 5. Worldwide Benchmarking From EY’s UK&I International Unit EY focus on working with NHS and other publicly funded organisations to: - Develop business cases and ‘go-to-market’ models for services - Develop pricing responses, investment requirements, effective financial risk mechanisms - Work with international private and public providers of healthcare to assist them in understanding their operational performance efficiency	As above, the lifecycle of EY engagements are such that at any one time EY are in scoping, design, delivery and sustainability phases across a number of projects in the country. The nature of EY’s work is to help providers and commissioners identify areas of poor performance or poor efficiency and work with them to improve. Some of EY’s projects are subject to tight confidentiality agreements and the scope/client is not known to that outside of the immediate engagement team and therefore EY cannot disclose this to others. These activities are essential to the future of the NHS – without efficiency use of NHS resources patient care will suffer and waiting lists grow. It is important that EY are able to provide EY’s clients with relevant data around the performance of other NHS trusts so that suitable benchmarks and improvement targets can be identified. It is also important that data outlining flows of patients around the NHS are available to EY’s clients to help them understand what services they need to provide and where. This information is reliant on a national data set but it is not reliant on the provision of patient level data to EY’s clients. Therefore EY need access to the full PbR dataset in-house, but EY clients and the wider project teams need only to work with the derivative data and clients will not receive patient-level data. Generally EY observe benefit realisations in the following areas (Subject to terms and scope of contract): 1. Performance Optimisation - Cost efficiencies to enable financial stability - Improve quality and patient experience - Meeting access targets 2. Integration and Restructuring - Improvements to clinical models - Compliance with Treasury Green Book - Cost efficiencies to enable financial stability - Improve quality and patient experience 3. Local Health Economy Transformation - Recognise achievements against national targets - Scenario analysis to identify efficiency improvements - Pathway reconfiguration - Commissioner Intentions setting 4. Economics and Pricing - Identify eligibilities for top up funding - Financial stability through coding due diligence - Activity plan development 5. Worldwide Benchmarking - Improvement in clinical productivity - Innovative international best practise benchmarking - Market analysis on a like-for-like basis internationally for key performance benchmarks - Non-NHS income generation for NHS organisations Commercial statement: This data will be used most commonly for EY analysis and understand the relative performance of organisations and health economies. The data will be used to support EY’s final work products but in most instances this will not be the sole purpose for which EY have been commissioned. If the data is used as part of a thought leadership piece, then the data source will be clearly referenced.
ERNST AND YOUNG LLP	ERNST AND YOUNG LLP	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Ernst and Young LLP (EY) work with a number of providers and funders of NHS care across the NHS spread across England, Wales and Scotland along with national bodies as listed https://www.gov.uk/government/publications/arms-lengthbodies/ our-arms-length-bodies . In addition, EY works with international healthcare organisations (this does not include device or pharmaceutical companies or health insurers). The work carried out for both types of clients is aimed at optimising performance, and having access to detailed information (e.g. benchmarking relating to NHS Trusts) is key to this. EY use the data to calculate relevant local and national Key Performance Indicators to share with clients and to bring about change within their clients. EY's request for these SUS PbR data sets is so that EY can quickly, and with insight, be responsive to tenders from the whole health and social care community and economy. More information can be found at http://www.ey.com/UK/en/Industries/Government---Public-Sector/Healthcare Around 50% of tenders the EY health team responded to last year for the UK&I business were contracted under the Consultancy ONE framework. This is a framework to which EY has been appointed by the Cabinet Office to be able to tender for services. There are no more than 20 suppliers nationally for each lot and EY has had to undergo a rigorous process of vetting by the Cabinet Office / Government Procurement Services to be eligible to respond to tenders released under this framework. Of the remainder, some are contracted under smaller frameworks such as gCloud, or via locally tendered/uncontested work outside a framework and thus contracted directly with the healthcare organisation. The tender mechanism does not differ depending on the type of service contracted. EY work on a wide variety of projects under these frameworks and all are slightly different in nature, owing to the needs of the NHS tendering, however, the majority of which fall into 5 categories: 1. Performance improvement: Assisting organisations in improvements in cost, outcomes and clinical pathways a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations in finding opportunities to improve from a cost, clinical pathway or outcome perspective b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 2. Integration and Restructuring 􀍴􀀃Assisting organisations who are planning to merge or partner, and working with providers who are close to or entering the failure regime. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using aggregate benchmark data in order to assist organisations to identify opportunities to merge or partner and to assist organisations to navigate the failure regime and also assisting organisations in understanding what a new merged organisation performance could look like. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 3. Local Health Economy Transformation - Understanding of capacity and demand and financial balance across a whole system or health economy a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations to better understand the demand across the whole system or health economy b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 4. Economics and Pricing. Working with national bodies such as NHS Improvement and NHS England on understanding the impact of local and national pricing decisions. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using national data to quantify the impact and understand the effects of policy decisions such as understanding the impact or adequacy of top-up payments. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 5. Worldwide Benchmarking To provide international benchmarks in areas such as Length of Stay and gross volume data to NHS organisations working overseas, for UK national government bodies such as UKTI, for clients near to UK such as in the Channel Islands, Ireland and for wider international comparison. We are also starting to work with healthcare providers in other countries who interested in their international performance. a. Aggregated benchmarking data will be provided to these organisations with the understanding that they consent to their data being used to benchmark against NHS clients. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients. c. EY has actively invested in developing its international capability. This has been focussed on working with NHS organisations, which includes Trusts, Academic Health Science Networks, and HealthcareUK, to support them to take their training, education and clinical operation capabilities to new markets. This is part of a wider UK PLC and public sector push, as demonstrated by the role of HealthcareUK, jointly sponsored by the DoH and NHSE. These clients make up around 10% of EY's revenue base at the moment. The intention is to provide aggregate (e.g. ICD/OPCS and POD level (or similar) benchmarks to these clients to help these providers or commissioners to improve their performance. This will benefit the NHS by having international comparators in return to understand international best practice. d. When working internationally with NHS organisations there have been frequent questions around how NHS performance compares with that of the host country. NHS Digital data will support that benchmarking, which in turn can support with the development of (i) feasibility studies (in collaboration with OECD, WHO and European monitoring systems), (ii) operational models, (iii) development of new healthcare facilities. In turn these will all support increased revenues to the NHS organisations, alongside options for organisations to support their education and research agenda and the reputation of the NHS and EY globally. e. The processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. The client base of all UK&I EY Advisory (as at January 2017) is split as follows: 31 Acute providers 3 CSUs 14 CCGs 17 Mental Health and/or Community providers 1 Ambulance Trust Regarding category 5) Worldwide Healthcare clients, our client list as at January 2017: 2 NHS organisations working overseas 5 Canadian Healthcare clients HealthcareUK / UK Trade and Investment (UKTI) Client in the Channel Islands (Publicly funded hospital) 10 US clients EY use the data in a variety of ways on these projects. For example EY would use it for basic benchmarking on Performance Improvement and for an Integration project EY would use the first 4 digits of postcodes to look at the site where fewest people would have to travel to attend. This is dependent on the engagement agreements EY have in place for each of these pieces of work. EY share results in aggregate form only. All outputs will have small numbers suppressed and will follow the HES Analysis Guide. EY do not share raw data. For the overseas clients, the processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. Data will not be used within EY for proactive targeting of prospective clients, but will for the fulfilling of client requirements as stated within this purpose; including responding to tenders for service and in external thought leadership production. External thought leadership consists of reports written to encourage thoughtful discussion and that are published for an audience of interested parties. An example being understanding the levels of specialist activity in non-specialist trust. Only aggregate data will be used and copyright for the data will be attributed to NHS Digital.	Data will be obtained from NHS Digital in a pseudonymised form and uploaded to a secure environment. From here the data will be manipulated to be integrated into the EY Health Analytics Data Platform, where it can be accessed by the end users in alignment with the small numbers policy. The NHS Digital data will not be linked with any other personal data. EY and Rackspace are both ISO 27001 compliant. The derived data will always be aggregated. Patient level data will not be transferred off the servers. All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. No data will be linked to record patient level data, and record-level data will not be removed from the secure servers. There will be two types of users: - Standard users are EY staff and will only have access to aggregated data (such as HRG level benchmarks) with small numbers suppressed and be able to change the view of such data that to be most useful to the client for purposes outlined earlier in section 5; - Super-users (also EY staff) and able to access raw patient level data. The named users will be limited (up to a maximum of 20) and will access the data set remotely via a secure, encrypted channel. Authorisation controls will be in place to ensure that named users have permissions which restrict them to access only the data designated for their access. This is ensured using role based permissions set up on the EY Active Directory server, a log and audit trail of access and data downloads is maintained and regularly monitored. Only data aggregated in line with the HES analysis guide may be downloaded. Data will not leave the EEA unless aggregated and in line with the HES analysis guide. Benchmarks • National benchmarks, for example day case rates or mortality rates, will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software for communication to EY colleagues and clients. Access by Superusers • Superusers must access the data from the UK. • Superusers of the analysis are EY employees only, accessing at the addresses stated for processing – giving access to the patient level data to any other group would be subject to a further application to DARS (and only given once an approval had been received). • Access to the patient level data by super users will be via an encrypted secure remote access channel, allowing only those with the agreed credentials to view the toolsets and applications within the Health analytics database. • The data will always remain resident within the data centre and will be manipulated remotely via Virtual Desktop Interface (VDI) protocol. This is particularly important in relation to users of the data for purpose 5, as it ensures that no data leaves the UK and that the data is observed through a window and manipulated on the UK based database server. • All superusers are EY UK Staff. Access by standard users • Standard users will only be able to access aggregated data will small numbers suppressed in line with advice from the HES analysis guide. • EY clients will have access to the aggregated outputs of analysis including benchmarks and visualisations. No patient level data will be available to clients. Other standard users will have access to aggregated benchmarks with small number suppression. Data Security Panel • EY will host an internal data security panel to review all requests for use of the national data. This panel will comprise a senior team to include QA and information governance leads, legal representative, and senior superusers. Where there are outstanding questions for non-standard requests, the panel will defer to NHS Digital for a decision. We have attached draft terms of reference for this panel for reference purposes. Further Security Information • EY have purchased a private space in the Rackspace cloud. This gives EY control over the locations where the data will be resident. • Cybersecurity protocols – Rackspace have agreed to additional third party security applications over and above their normal technical and operational security controls. This includes EY-managed encryption at rest, vulnerability scanning, privilege management and others. Applications and data are backed up using a dedicated Managed Backup facility at the Rackspace LON3 data centre in Slough. • All EY staff are subject to the global client confidentiality policy which outlines every employee’s responsibility with regards confidential information.	EY outputs are bespoke to each client and each engagement has their own milestones and delivery dates. These are ongoing. Client requested data will be transferred by EY employees to Excel or other visualisation software such as Spotfire or PowerPoint for communication to colleagues and clients. The outputs will be aggregated with small numbers suppressed. Patient level data will not be transferred off the servers. All outputs will follow the HES analysis guide. No data will be linked to record patient level data. All data extracts will be quality assured by a senior member of the EY team before being used to deliver the scope of work agreed with the client. The following outputs may apply depending upon the individual service requested. - Benchmarking applies across all services. National benchmarks will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software including EY’s health platform for communication to EY colleagues and clients. - The derived data will always be aggregated. - Outputs will be available as per the scope of services and engagement letter but is usually the Board (including non-executive members) and service managers/clinical directors. 1. Performance Optimisation - Reports – A summary of outputs outlined below which may be made available to third parties such as regulators (NHS Improvement, TDA etc) - Benchmarking – e.g.. Showing an organisations position against selected peer group or national average for DNA rates - Drive Time Analysis – e.g. heat maps to show where patients are travelling from to access services to understand whether outreach clinics would be more accessible to patients - Performance Optimisation Dashboards – Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking. 2. Integration and Restructuring - Reports – EY may be asked by a regulator or organisation public sector to form a judgement on the future sustainability of their organisation and the options available if it is deemed not viable in the current form. - Benchmarking – If two or more organisations are merging then it’s useful for them to have an understanding of their relative performance to each other which would be derived from local data but also to a new group of peers for a potential combined organisation to enable the boards to understand how they would compare. - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction pre and/or post merger - Local Health Economy Plan – If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 3. Local Health Economy Transformation - Reports – EY are asked to size the financial gap in a health economy and then provide a view on how to close the gap, some of this can be through understanding differences in activity and efficiency for different providers in the patch. - Benchmarking – Aggregated benchmarking for commissioners and providers (at HRG/POD level) allows the identification of different pathways of care and health inequalities amongst the local population - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction - Local Health Economy Plan - If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains which are often similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 4. Economics and Pricing - Reports – An example of the type of report EY are asked to compile is using PLICs or reference costs for providers and examining the margins associated with particular HRGs or specialties, in instances such as this EY would be using NHS Digital data to identify peers using a co-morbidity coefficient or similar. - Benchmarking – Linked to the point above, EY would be using NHS Digital data to identify peers and possible reasons for cost drivers such as average bed days, demographics etc. - Size Impact of tariff change to local and national NHS organisations – Where a change in the tariff, such as the application of a top up tariff or an agreement of block funding is indicated based on a review of PLICs data then the HRG volume information would be used to estimate future possible cost to commissioners and income for the provider. This can be used to develop an evidence based case for the commissioner. 5. Worldwide Benchmarking From EY’s UK&I International Unit EY focus on working with NHS and other publicly funded organisations to: - Develop business cases and ‘go-to-market’ models for services - Develop pricing responses, investment requirements, effective financial risk mechanisms - Work with international private and public providers of healthcare to assist them in understanding their operational performance efficiency	As above, the lifecycle of EY engagements are such that at any one time EY are in scoping, design, delivery and sustainability phases across a number of projects in the country. The nature of EY’s work is to help providers and commissioners identify areas of poor performance or poor efficiency and work with them to improve. Some of EY’s projects are subject to tight confidentiality agreements and the scope/client is not known to that outside of the immediate engagement team and therefore EY cannot disclose this to others. These activities are essential to the future of the NHS – without efficiency use of NHS resources patient care will suffer and waiting lists grow. It is important that EY are able to provide EY’s clients with relevant data around the performance of other NHS trusts so that suitable benchmarks and improvement targets can be identified. It is also important that data outlining flows of patients around the NHS are available to EY’s clients to help them understand what services they need to provide and where. This information is reliant on a national data set but it is not reliant on the provision of patient level data to EY’s clients. Therefore EY need access to the full PbR dataset in-house, but EY clients and the wider project teams need only to work with the derivative data and clients will not receive patient-level data. Generally EY observe benefit realisations in the following areas (Subject to terms and scope of contract): 1. Performance Optimisation - Cost efficiencies to enable financial stability - Improve quality and patient experience - Meeting access targets 2. Integration and Restructuring - Improvements to clinical models - Compliance with Treasury Green Book - Cost efficiencies to enable financial stability - Improve quality and patient experience 3. Local Health Economy Transformation - Recognise achievements against national targets - Scenario analysis to identify efficiency improvements - Pathway reconfiguration - Commissioner Intentions setting 4. Economics and Pricing - Identify eligibilities for top up funding - Financial stability through coding due diligence - Activity plan development 5. Worldwide Benchmarking - Improvement in clinical productivity - Innovative international best practise benchmarking - Market analysis on a like-for-like basis internationally for key performance benchmarks - Non-NHS income generation for NHS organisations Commercial statement: This data will be used most commonly for EY analysis and understand the relative performance of organisations and health economies. The data will be used to support EY’s final work products but in most instances this will not be the sole purpose for which EY have been commissioned. If the data is used as part of a thought leadership piece, then the data source will be clearly referenced.
ERNST AND YOUNG LLP	ERNST AND YOUNG LLP	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Ernst and Young LLP (EY) work with a number of providers and funders of NHS care across the NHS spread across England, Wales and Scotland along with national bodies as listed https://www.gov.uk/government/publications/arms-lengthbodies/ our-arms-length-bodies . In addition, EY works with international healthcare organisations (this does not include device or pharmaceutical companies or health insurers). The work carried out for both types of clients is aimed at optimising performance, and having access to detailed information (e.g. benchmarking relating to NHS Trusts) is key to this. EY use the data to calculate relevant local and national Key Performance Indicators to share with clients and to bring about change within their clients. EY's request for these SUS PbR data sets is so that EY can quickly, and with insight, be responsive to tenders from the whole health and social care community and economy. More information can be found at http://www.ey.com/UK/en/Industries/Government---Public-Sector/Healthcare Around 50% of tenders the EY health team responded to last year for the UK&I business were contracted under the Consultancy ONE framework. This is a framework to which EY has been appointed by the Cabinet Office to be able to tender for services. There are no more than 20 suppliers nationally for each lot and EY has had to undergo a rigorous process of vetting by the Cabinet Office / Government Procurement Services to be eligible to respond to tenders released under this framework. Of the remainder, some are contracted under smaller frameworks such as gCloud, or via locally tendered/uncontested work outside a framework and thus contracted directly with the healthcare organisation. The tender mechanism does not differ depending on the type of service contracted. EY work on a wide variety of projects under these frameworks and all are slightly different in nature, owing to the needs of the NHS tendering, however, the majority of which fall into 5 categories: 1. Performance improvement: Assisting organisations in improvements in cost, outcomes and clinical pathways a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations in finding opportunities to improve from a cost, clinical pathway or outcome perspective b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 2. Integration and Restructuring 􀍴􀀃Assisting organisations who are planning to merge or partner, and working with providers who are close to or entering the failure regime. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using aggregate benchmark data in order to assist organisations to identify opportunities to merge or partner and to assist organisations to navigate the failure regime and also assisting organisations in understanding what a new merged organisation performance could look like. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 3. Local Health Economy Transformation - Understanding of capacity and demand and financial balance across a whole system or health economy a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include providing aggregate benchmark data in order to assist organisations to better understand the demand across the whole system or health economy b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 4. Economics and Pricing. Working with national bodies such as NHS Improvement and NHS England on understanding the impact of local and national pricing decisions. a. The intention for use of data in this case is varied and will depend on the engagement agreement in place with the client. A typical engagement may include using national data to quantify the impact and understand the effects of policy decisions such as understanding the impact or adequacy of top-up payments. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients 5. Worldwide Benchmarking To provide international benchmarks in areas such as Length of Stay and gross volume data to NHS organisations working overseas, for UK national government bodies such as UKTI, for clients near to UK such as in the Channel Islands, Ireland and for wider international comparison. We are also starting to work with healthcare providers in other countries who interested in their international performance. a. Aggregated benchmarking data will be provided to these organisations with the understanding that they consent to their data being used to benchmark against NHS clients. b. Only aggregated data with small numbers suppressed in line with the HES Analysis Guide will be provided to these clients. c. EY has actively invested in developing its international capability. This has been focussed on working with NHS organisations, which includes Trusts, Academic Health Science Networks, and HealthcareUK, to support them to take their training, education and clinical operation capabilities to new markets. This is part of a wider UK PLC and public sector push, as demonstrated by the role of HealthcareUK, jointly sponsored by the DoH and NHSE. These clients make up around 10% of EY's revenue base at the moment. The intention is to provide aggregate (e.g. ICD/OPCS and POD level (or similar) benchmarks to these clients to help these providers or commissioners to improve their performance. This will benefit the NHS by having international comparators in return to understand international best practice. d. When working internationally with NHS organisations there have been frequent questions around how NHS performance compares with that of the host country. NHS Digital data will support that benchmarking, which in turn can support with the development of (i) feasibility studies (in collaboration with OECD, WHO and European monitoring systems), (ii) operational models, (iii) development of new healthcare facilities. In turn these will all support increased revenues to the NHS organisations, alongside options for organisations to support their education and research agenda and the reputation of the NHS and EY globally. e. The processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. The client base of all UK&I EY Advisory (as at January 2017) is split as follows: 31 Acute providers 3 CSUs 14 CCGs 17 Mental Health and/or Community providers 1 Ambulance Trust Regarding category 5) Worldwide Healthcare clients, our client list as at January 2017: 2 NHS organisations working overseas 5 Canadian Healthcare clients HealthcareUK / UK Trade and Investment (UKTI) Client in the Channel Islands (Publicly funded hospital) 10 US clients EY use the data in a variety of ways on these projects. For example EY would use it for basic benchmarking on Performance Improvement and for an Integration project EY would use the first 4 digits of postcodes to look at the site where fewest people would have to travel to attend. This is dependent on the engagement agreements EY have in place for each of these pieces of work. EY share results in aggregate form only. All outputs will have small numbers suppressed and will follow the HES Analysis Guide. EY do not share raw data. For the overseas clients, the processing activities would be England based (London) and aggregated outputs from these would be made available to the clients. Data will not be used within EY for proactive targeting of prospective clients, but will for the fulfilling of client requirements as stated within this purpose; including responding to tenders for service and in external thought leadership production. External thought leadership consists of reports written to encourage thoughtful discussion and that are published for an audience of interested parties. An example being understanding the levels of specialist activity in non-specialist trust. Only aggregate data will be used and copyright for the data will be attributed to NHS Digital.	Data will be obtained from NHS Digital in a pseudonymised form and uploaded to a secure environment. From here the data will be manipulated to be integrated into the EY Health Analytics Data Platform, where it can be accessed by the end users in alignment with the small numbers policy. The NHS Digital data will not be linked with any other personal data. EY and Rackspace are both ISO 27001 compliant. The derived data will always be aggregated. Patient level data will not be transferred off the servers. All outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. No data will be linked to record patient level data, and record-level data will not be removed from the secure servers. There will be two types of users: - Standard users are EY staff and will only have access to aggregated data (such as HRG level benchmarks) with small numbers suppressed and be able to change the view of such data that to be most useful to the client for purposes outlined earlier in section 5; - Super-users (also EY staff) and able to access raw patient level data. The named users will be limited (up to a maximum of 20) and will access the data set remotely via a secure, encrypted channel. Authorisation controls will be in place to ensure that named users have permissions which restrict them to access only the data designated for their access. This is ensured using role based permissions set up on the EY Active Directory server, a log and audit trail of access and data downloads is maintained and regularly monitored. Only data aggregated in line with the HES analysis guide may be downloaded. Data will not leave the EEA unless aggregated and in line with the HES analysis guide. Benchmarks • National benchmarks, for example day case rates or mortality rates, will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software for communication to EY colleagues and clients. Access by Superusers • Superusers must access the data from the UK. • Superusers of the analysis are EY employees only, accessing at the addresses stated for processing – giving access to the patient level data to any other group would be subject to a further application to DARS (and only given once an approval had been received). • Access to the patient level data by super users will be via an encrypted secure remote access channel, allowing only those with the agreed credentials to view the toolsets and applications within the Health analytics database. • The data will always remain resident within the data centre and will be manipulated remotely via Virtual Desktop Interface (VDI) protocol. This is particularly important in relation to users of the data for purpose 5, as it ensures that no data leaves the UK and that the data is observed through a window and manipulated on the UK based database server. • All superusers are EY UK Staff. Access by standard users • Standard users will only be able to access aggregated data will small numbers suppressed in line with advice from the HES analysis guide. • EY clients will have access to the aggregated outputs of analysis including benchmarks and visualisations. No patient level data will be available to clients. Other standard users will have access to aggregated benchmarks with small number suppression. Data Security Panel • EY will host an internal data security panel to review all requests for use of the national data. This panel will comprise a senior team to include QA and information governance leads, legal representative, and senior superusers. Where there are outstanding questions for non-standard requests, the panel will defer to NHS Digital for a decision. We have attached draft terms of reference for this panel for reference purposes. Further Security Information • EY have purchased a private space in the Rackspace cloud. This gives EY control over the locations where the data will be resident. • Cybersecurity protocols – Rackspace have agreed to additional third party security applications over and above their normal technical and operational security controls. This includes EY-managed encryption at rest, vulnerability scanning, privilege management and others. Applications and data are backed up using a dedicated Managed Backup facility at the Rackspace LON3 data centre in Slough. • All EY staff are subject to the global client confidentiality policy which outlines every employee’s responsibility with regards confidential information.	EY outputs are bespoke to each client and each engagement has their own milestones and delivery dates. These are ongoing. Client requested data will be transferred by EY employees to Excel or other visualisation software such as Spotfire or PowerPoint for communication to colleagues and clients. The outputs will be aggregated with small numbers suppressed. Patient level data will not be transferred off the servers. All outputs will follow the HES analysis guide. No data will be linked to record patient level data. All data extracts will be quality assured by a senior member of the EY team before being used to deliver the scope of work agreed with the client. The following outputs may apply depending upon the individual service requested. - Benchmarking applies across all services. National benchmarks will be derived from the national data and stored on the same servers as the raw data with the same level of security. The outputs from queries against these data will be transferred to excel or visualisation software including EY’s health platform for communication to EY colleagues and clients. - The derived data will always be aggregated. - Outputs will be available as per the scope of services and engagement letter but is usually the Board (including non-executive members) and service managers/clinical directors. 1. Performance Optimisation - Reports – A summary of outputs outlined below which may be made available to third parties such as regulators (NHS Improvement, TDA etc) - Benchmarking – e.g.. Showing an organisations position against selected peer group or national average for DNA rates - Drive Time Analysis – e.g. heat maps to show where patients are travelling from to access services to understand whether outreach clinics would be more accessible to patients - Performance Optimisation Dashboards – Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking. 2. Integration and Restructuring - Reports – EY may be asked by a regulator or organisation public sector to form a judgement on the future sustainability of their organisation and the options available if it is deemed not viable in the current form. - Benchmarking – If two or more organisations are merging then it’s useful for them to have an understanding of their relative performance to each other which would be derived from local data but also to a new group of peers for a potential combined organisation to enable the boards to understand how they would compare. - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction pre and/or post merger - Local Health Economy Plan – If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 3. Local Health Economy Transformation - Reports – EY are asked to size the financial gap in a health economy and then provide a view on how to close the gap, some of this can be through understanding differences in activity and efficiency for different providers in the patch. - Benchmarking – Aggregated benchmarking for commissioners and providers (at HRG/POD level) allows the identification of different pathways of care and health inequalities amongst the local population - Drive Time Analysis - e.g. heat maps to show where patients are travelling from to e.g. heat maps to show where patients are travelling from to access services to understand the potential impact of a site reconfiguration or change in service provider - Performance Optimisation Dashboards - Design and delivery of dashboards to be used by the organisation to track progress against targets agreed as part of benchmarking piece of work or to deliver cost reduction - Local Health Economy Plan - If a health economy jointly commissions an overarching review they often request benchmarking of local providers in the domains which are often similar to the BCBV indicators to understand the totality of the local picture. They may also wish to understand simulation models such as when an A&E closes, the possible impact on the surrounding providers though looking at activity trends and postcodes of conveyance. 4. Economics and Pricing - Reports – An example of the type of report EY are asked to compile is using PLICs or reference costs for providers and examining the margins associated with particular HRGs or specialties, in instances such as this EY would be using NHS Digital data to identify peers using a co-morbidity coefficient or similar. - Benchmarking – Linked to the point above, EY would be using NHS Digital data to identify peers and possible reasons for cost drivers such as average bed days, demographics etc. - Size Impact of tariff change to local and national NHS organisations – Where a change in the tariff, such as the application of a top up tariff or an agreement of block funding is indicated based on a review of PLICs data then the HRG volume information would be used to estimate future possible cost to commissioners and income for the provider. This can be used to develop an evidence based case for the commissioner. 5. Worldwide Benchmarking From EY’s UK&I International Unit EY focus on working with NHS and other publicly funded organisations to: - Develop business cases and ‘go-to-market’ models for services - Develop pricing responses, investment requirements, effective financial risk mechanisms - Work with international private and public providers of healthcare to assist them in understanding their operational performance efficiency	As above, the lifecycle of EY engagements are such that at any one time EY are in scoping, design, delivery and sustainability phases across a number of projects in the country. The nature of EY’s work is to help providers and commissioners identify areas of poor performance or poor efficiency and work with them to improve. Some of EY’s projects are subject to tight confidentiality agreements and the scope/client is not known to that outside of the immediate engagement team and therefore EY cannot disclose this to others. These activities are essential to the future of the NHS – without efficiency use of NHS resources patient care will suffer and waiting lists grow. It is important that EY are able to provide EY’s clients with relevant data around the performance of other NHS trusts so that suitable benchmarks and improvement targets can be identified. It is also important that data outlining flows of patients around the NHS are available to EY’s clients to help them understand what services they need to provide and where. This information is reliant on a national data set but it is not reliant on the provision of patient level data to EY’s clients. Therefore EY need access to the full PbR dataset in-house, but EY clients and the wider project teams need only to work with the derivative data and clients will not receive patient-level data. Generally EY observe benefit realisations in the following areas (Subject to terms and scope of contract): 1. Performance Optimisation - Cost efficiencies to enable financial stability - Improve quality and patient experience - Meeting access targets 2. Integration and Restructuring - Improvements to clinical models - Compliance with Treasury Green Book - Cost efficiencies to enable financial stability - Improve quality and patient experience 3. Local Health Economy Transformation - Recognise achievements against national targets - Scenario analysis to identify efficiency improvements - Pathway reconfiguration - Commissioner Intentions setting 4. Economics and Pricing - Identify eligibilities for top up funding - Financial stability through coding due diligence - Activity plan development 5. Worldwide Benchmarking - Improvement in clinical productivity - Innovative international best practise benchmarking - Market analysis on a like-for-like basis internationally for key performance benchmarks - Non-NHS income generation for NHS organisations Commercial statement: This data will be used most commonly for EY analysis and understand the relative performance of organisations and health economies. The data will be used to support EY’s final work products but in most instances this will not be the sole purpose for which EY have been commissioned. If the data is used as part of a thought leadership piece, then the data source will be clearly referenced.
GENOMICS ENGLAND	GENOMICS ENGLAND	Hospital Episode Statistics Accident and Emergency	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Hospital Episode Statistics Critical Care	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Hospital Episode Statistics Outpatients	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Diagnostic Imaging Dataset	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Mental Health Minimum Data Set	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set	Anonymised - ICO code compliant	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Mental Health and Learning Disabilities Data Set	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Patient Reported Outcome Measures (Linkable to HES)	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
GENOMICS ENGLAND	GENOMICS ENGLAND	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The aim is to create a new genomic medicine service for the NHS – transforming the way people are cared for. Patients may be offered a diagnosis where there wasn’t one before. In time, there is the potential of new and more effective treatments. The project will also enable new medical research. Combining genomic sequence data with medical records is a ground-breaking resource. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. We also aim to kick-start a UK genomics industry. This is currently the largest national sequencing project of its kind in the world. Genomics England seeking to obtain information from participants’ medical records that span their entire lifetime. The DNA sequence, and information from patients’ health records and any other information given to the Project will be collected and stored securely by the Project as a resource for use by approved researchers for future scientific and medical purposes during the life and after the death of participants. Diagnoses arising from the sequencing and analysis of the participants’ DNA are already being fed back to Participants now and for many they are receiving a diagnosis for the first time. Genomic England’s legacy will be a genomics service ready for adoption by the NHS, high ethical standards and public support for genomics, new medicines, treatments and diagnostics and a country which hosts the world’s leading genomic companies. It is a bold ambition with benefits for all. Updates to include new data sets (October 2016): PROMS: This dataset provides valuable data for calculating the incidence rates for thromboembolic and bleeding outcomes in these patients. Example for clinical utility: Without predisposing risk factors, the frequency of venous thromboembolisms after major orthopaedic surgery is high and most guidelines recommend the use of prophylaxis. Despite prophylaxis, a small number of patients develops venous thromboembolism and requires a therapeutic dose of anticoagulation. Unfortunately, for some of these patients this leads to severe bleeding-related complications. This dataset will allow performing correlative analyses between bleeding/thrombotic outcomes and the genomic biomarker data. Identifying the genomic biomarkers that lead to increase of bleeding or thrombosis will help improving the current bleeding risk models. Example for use in research: Useful data set to look at pain related genetics Example for clinical utility: Risk models indicating likelihood of success are especially valuable for elective surgery. Utilizing different machine learning/Predictive platforms to build such models would create directionality in future treatment strategies as well as benchmark alternatives. The PROMS dataset in combination with other medical records will enable identification patients based on clinical phenotype that are most likely to benefit. On the other hand, patients least likely to benefit will also be identified to assist in clinical decision making in collaboration with the patient. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (eg pain response etc). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. While there is unlikely to be direct utility in Cancer, within rare diseases, there will be a number of these surgeries which are treatments of manifestations of the disease (for example multiple epiphyseal dysplasia and other skeletal dyplasiaa for joint replacements). These data sets will help in research on outcomes of these treatments. ONS Mortality Data: The data this provides is essential for performing survival analyses (Death dates) and so represents a very valuable source of data. This dataset is extremely critical because mortality information is a surrogate metric for success of medical care. Therefore this dataset will enable identification of factors that drive successful treatment of a patient. For industry, combined with other medical history this is a critical piece of information. There are many primary endpoints cancer researchers are concerned with when analysing follow up data (response to treatment, time to relapse) but the primary endpoint of most concern not just to academics and clinicians, but also to patients, is time to death (and related to this, specific cause of death). Clinicians know that 2 patients presenting with the same cancer at the same stage can have very different outcomes, but they don’t understand why. The capability to link genomic with mortality data could provide valuable insights into how and why this occurs. Cause of death is also crucial for Genomics to access, since if a patient dies of an illness, or due to an accident, entirely unrelated to their cancer, they would want to know so that the mortality data does not get skewed by these instances. Conversely, researchers may identify trends in non-cancer causes of death in particular groups of cancer patients. Genomics therefore would not want to limit their access just to date of death for this reason. As alluded to above, in order to correlate mortality data with other clinical and genomic data, Genomics would require mortality data linked by NHS number. There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate phenotypes with genomic markers (e.g different causes of death). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non-hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records ). The greatest utility for these more general uses is in combination with several datasets. This dataset is a critical component of the life course data for Rare Diseases. Many rare diseases are life-limiting and it is important to document when premature death has occurred and correlate this with genomically-defined subtypes of the disease. DIDS: Diagnostic imaging data is an invaluable part of data representing a patient’s medical condition. Findings from imaging can be used for personalized medicine for applications such as identifying the best treatment pathway. While the imaging dataset itself is extremely data-rich, access to notes by a radiologist or pathologist will be more useable in the short-term. Clinical records are important, however, imaging data serves the bridge to molecular data. Additionally, imaging data serves as the new frontier in biomarker development as well as stratification of future potential patients for pharma that make seek benefit using Precision Medicine tools. Cancer: This dataset would be useful for RECIST (Response Evaluation Criteria In Solid Tumours) calculations and other parameters potentially useful for cancer. Imaging data would be high priority and potentially of very high value to refine phenotypes. Imaging does not just inform about tumour size but can also inform about extent of spread. This may be academically interesting on an individual basis, but on a cohort basis could provide insights into whether particular genomic alterations are associated with a particular tumour widely metastasising, metastasising to particular sites, metastasising early in the disease history, or indeed, not metastasising. An example of how such findings could translate into improvements in clinical care in the future may be rationalising the type and intensity of imaging surveillance schedules in light of diagnosis and genomic alterations. Only a comprehensive record of diagnostic imaging tests carried out on patients can provide Genomics with the capability to understand the patients imaging, and consequently, cancer history, and provide them with the potential to look into the outcomes associated with imaging investigations. Access to this central dataset is particularly important since cancer patients often have imaging investigations at a number of different sites / organisations, so the centre where the patient is recruited to the project could not inform us about the patients comprehensive imaging record. In terms of limiting the amount of data requested, Genomics could limit our access to those imaging studies where a lesion likely representing cancer was found, and which were carried out to investigate a previously detected tumour. However, since the DID does not store information on the outcomes or diagnoses related to these images, Genomics cannot limit their access request to those particular imaging records. It is crucial that imaging test history is linked to the NHS number, since the NHS number is the single consistent identifier used by Genomics England to link all of the participants data. An imaging record without the NHS number would mean that Genomics could not correlate the imaging record with the genomic data, which is where the value of the dataset lies, since the research interest is predominantly in how genomic alterations are associated with cancer development and progression, the latter characterised by imaging studies. Rare Disease: This dataset will be important to additionally characterise sub-elements of the rare disease phenotype. Currently the phenotype is only captured by the HPO terms: full imaging data will enable finer granularity than is afforded by the HPO terms as to the nature, location and severity of anatomical lesions. Improved detail on anatomical lesions will enhance precision for clinical diagnostics as well as for gene discovery and improved clinical characterisation of known predisposition/causative genes. Mental Health Data: There will be utility in provision of access to these data through the 100,000 Genomes Project for some population studies (which are agnostic of cancer or Rare Disease status). The aim of these would be to correlate these phenotypes with genomic markers (e.g. depression, anxiety etc.). There will also be utility in the provision of access to this data for a number of the more general ‘acceptable uses’ outlined in the 100,000 Genomes Project Protocol, such as non hypothesis driven research (such ‘big data’ and hypothesis generating research, clinical trial feasibility, research into electronic health records etc.). The greatest utility for these more general uses is in combination with several datasets. Whilst these data are unlikely to be directly relevant to most immediate analyses of Rare Disease and cancer, there will be some Rare Disease groups in which there can be adult-onset neuropsychiatric elements to the phenotype. This dataset would enhance the accuracy of the phenotypic life course analyses regarding the evolution and clinical progression of these neuropsychiatric features	The first stage focuses on the acquisition of data and quality verification to ensure it is complete, accurate and complies well with NHS data dictionary and other data standards that apply. The data is provided over a period of time (related to the treatment of participants) and associated with their longitudinal data from other NHS sources. The intention over the course of the Project is to link this data with other data, such as primary, secondary, social and participant provided data. Genomics England currently links to HES Data. This application builds on the origional application and requests linkage to the additional data sets defined here. The richness of the high quality data sets are crucial to the success of the 100,000 Genome Project in delivering value to the NHS. The evaluation of whole genome sequencing (WGS) data in the context of rich and extended phenotypes derived from electronic health records, such as blood pressure, cholesterol, glucose, and pharmacogenomics, adds significant value. The richness of the Project dataset will allow us to move beyond the primary phenotype of the rare disease, cancer or infectious disease that led to the patient’s enrolment to evaluate the WGS in the context of other continuous traits, diseases and response to therapy. As soon as the data completeness and quality has been confirmed the data is de-identified as all subsequent processing can be performed without direct identifiers. This de-identification is a key facet of the 100,000 Genomes Project. The second stage is focused on the confirmation and approval of valid research scope and selecting a de-identified cohort of participants that fulfil the focus of the research request. The Researchers will be members of a Genomics England Clinical Interpretation Partnership (GECiP) or a GENE Consortia. GECiP. The overall aim of the Genomics England Clinical Interpretation Partnership (GeCIP) is to create a thriving, sustainable environment for researchers and clinical (NHS) disease experts. The activities of GeCIP will inform NHS feedback to clinicians and the multidisciplinary teams by providing enhanced data interpretation, additional information on pathogenicity of variants, and functional characterisation. GENE Consortia. Genomics England are running an Industry trial during the calendar year 2016 that consists of 12 pharma, biotech and diagnostics companies committing to invest monetary and FTE resources to understand how best to realise the value from working with Genomics England, our Bioinformatics Platform Partners and the wider NHS. Across the 100,000 Genome Project Genomics England will be at the forefront of Lifescience Programmes in the UK and Worldwide. For example Gene discovery in the 100,000 Genomes Project will create significant opportunities for scientific innovation and place particular emphasis upon national and international collaborations. Where possible, we will work with key international programmes including Development Disorders (DDD) and Orphanet, and complement the work of the International Rare Diseases Consortium (IRDC). All research requests will be assessed to ensure they are included in the approved use purposes set out in the Genomics England Protocol and that it complies with the boundaries of the research group (Genomics England Clinical Interpretation Partnership or GENE consortia). Each research request will be for a sub-set of the de-identified data, with the specific data requirements specified in the request. The researchers also declare any data they wish to bring into the environment and any tools they wish to use for analysis. The third stage is the research analysis of the de-identified approved data sets in the virtual data centre environments. Researchers perform all the analysis and processing within the environments hosted by Genomics England, they do not extract de-identified data. Researchers will use pre-declared data and tools to perform their analysis. If researchers want to extract any anonymised results data, they must first put any such results in a secure folder for anonymisation verification before it can be extracted. Genomics England provide the HSCIC with a cohort for linkage and they receive data from the HSCIC on a monthly basis. Every quarter Genomics provide an updated cohort to the HSCIC and the HSCIC provide the historical data for the extra cohort members The cohort is already flagged with the HSCIC so Genomics will only receive the historical data for the extra cohort members each quarter.	All outputs from research environments will be anonymised. The outputs will relate to the purposes described above for each of the research areas. Proof of concept outputs will be produced during the summer of 2015, with a move to researcher created outputs during the Autumn of 2015 onwards. The specific outputs are defined by the research groups and then verified for being anonymous when an extract is requested. Update April 2017 Over 20,000 whole genomes have now been sequenced and we are making new and exciting discoveries that are changing the lives of participants. Genomics England are analysing the genome data together with health data, to help us find the cause of disease. The analysis is complex and takes many months but below are just a few of the life changing discoveries we have made. First diagnoses from the pilot phase https://www.genomicsengland.co.uk/first-patients-diagnosed-through-the-100000-genomes-project/ First children diagnosed through the project https://www.genomicsengland.co.uk/first-children-recieve-diagnoses-through-100000-genomes-project/ Financial Times article: https://www.ft.com/content/d2e21cea-d684-11e6-944b-e7eb37a6aa8e Other participant testimonies from GMCs: Toby: https://www.uhb.nhs.uk/news/cancer-patient-toby-explains-why-he-joined.htm Jackie: http://www.westmidsgmc.nhs.uk/patient-jackie-why-i-joined-pioneering-100000-genomes-project/ On a more practical side Genomics England have also seen the opening of the new Bridget Oglivivie Building at the Sanger Institute in Cambridge. This building opened by Teresa May on the 21st November houses the infrastructure used to sequence the DNA of the 100,000 Genome Project. Sir John Chisholm said “The UK is recognised across the world for being the first nation to introduce whole genome sequencing at scale in routine care environments. The Ogilvie facility is central to that achievement and opens the way to the virtuous circle of treating patients with genomic medicine leading to knowledge creation leading to advanced therapies leading to superior health outcomes”. Genomics England is at the forefront of the Governments Technology Development agenda and is driving forward world class research in the UK.	The overall benefits realisation for the project are established by the Department of Health (DoH). Each individual research study will have their own specific aims and benefits that underpin the DoH benefits. The 10 key benefits have been drafted as: 1. It is anticipated that many of the circa 20,000 patients with rare diseases who provide their genomes for sequencing as part of the Project will receive a formal diagnosis for the first time. 2. The speed of processing the data from Whole Genome Sequences should be greatly increased with an associated acceleration of diagnosis – something that previously has taken years to identify, under the Project this should be possible in a few months. 3. It is hoped that Genomic diagnosis as a result of the Project will enable clinicians to make cancer treatment more personalised by determining how effective treatments like Herceptin or radiotherapy are likely to be. This will improve the effectiveness of treatments and may provide financial savings. 4. Although not all patients involved in the Project will benefit from a significant improvement in their own condition, for most the benefit will be in knowing that they will be helping people like them in the future. 5. The Project has already identified issues with the current approach for collecting DNA from cancer tumours. A current study within the Project is looking at identifying optimum methods for collecting DNA from cancer tumours. This is something which previously that has been incredibly difficult to do at scale and which is essential for high quality Whole Genome Sequencing. 7. As a result of the high standards of ethical practice and transparency underpinning the Project, the case will be made for collecting genomic data, linking it the phenotypic data and sharing it in a controlled way with academics, researchers and industry. 8. The creation of NHS Genomic Medicine Centres will allow engagement and feedback to patients with rare diseases and cancer from the Project and will provide the infrastructure to bring about transformational change in the NHS so that it continues to deliver world-leading healthcare in the future 8. As a result of the Project, the NHS and Public Health workforce will benefit from additional education in genomic medicine, including 550 places for an MSc in Genomics Medicine over the next 3 years, increased capacity in the scientific workforce, and a legacy of education and training in genomics for the future workforce. 9. The secure dataset of genomic and clinical data which is created as a result of the Project will enable clinicians, researchers and industry to discover new variants with a view to creating new diagnostics and treatments. 10. The Project will kick-start the development of the UK industry in Whole Genome Sequencing. The global genomics market was valued at an estimated £7.6 billion in 2013 and is expected to reach over £13 billion by 2018.
HARVEY WALSH LTD	HARVEY WALSH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Harvey Walsh is an IT solutions, NHS Data and Health Informatics Consultancy which provides services to the NHS, Academic Health Science Networks (AHSNs) Pharmaceutical and Device Industry, Patient Groups and Healthcare Charities. Harvey Walsh uses pseudonymised, monthly refreshed HES data to undertake analysis, develop services and provide solutions to support commissioning, analytical support and outcomes analysis for NHS organisations, which include NHS England, the NHS Board, GP Practices, GP Federations, CCGs, Regional Teams, Academic Health Science Networks, Health and Well-Being Boards, Provider Trusts, Ambulance Trusts, CSUs and Sustainability and Transformation Partnerships (STPs). The services and solutions provided include analysis and outputs for the purposes of informing commissioners, healthcare professionals and clinical networks on uptake on innovation national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. Harvey Walsh also utilise these HES data to provide services to commercial organisations within the pharmaceutical, medical device industry, patient organisations, healthcare charities and AHSNs. These organisations use the outputs and insights provided by Harvey Walsh to work collaboratively with NHS organisations to promote health and improve the well-being of patients. The use of the data supports the development of innovative solutions and service improvement, to track outcomes and provide the real world evidence as required by the NHS, NICE, Monitor and NHS England. The aim of which is to improve patient care and support enhanced access to improved services and innovative solutions. In addition these clients use these outputs for the purposes of providing supporting information required by the NHS for business cases, epidemiological research, pathway analysis, burden of disease analysis, health economic research, predictive analytical modelling, NICE submissions and quality and outcome analysis. The outputs of which are shared directly or indirectly with the NHS to support improvements in patient care. All outputs are aggregated with small numbers suppressed in line with the HES analysis guide. In addition to this analytical work, Harvey Walsh also provide a tool called AXON 360. AXON 360 is an online tool that is hosted within the Harvey Walsh offices in a secure server room from a dedicated network. AXON 360 incorporates aggregated HES, QOF and primary care prescribing data, this tool allows users to analyse healthcare data, derive a range of insights and produce reports to support improvements in patient care and provide insights and evidence for commissioners and healthcare professionals. Data is also analysed for the purposes of informing commissioners, clinicians and clinical networks on uptake on innovation, national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. All data is aggregated and small numbers are suppressed, including secondary suppression as per the HES analysis guide. The outputs that utilize the HES data are provided directly to the NHS and AHSNs or will be provided indirectly to the NHS via the pharmaceutical and device companies and AHSNs using bespoke outputs, reports, dashboards, research papers and via a tool called AXON 360 produced by Harvey Walsh. All of the outputs from the analysis of HES are used directly or indirectly for the provision or promotion of health and improved patient care and are not wholly commercial. Each purpose within this application (including the Axon tool) other than one specific project stated here (AF project) are only permitted to use 5 years of HES data. An AF project is already underway and is looking at temporal trends over the past 10 years. The project is a collaboration between Harvey Walsh, Greater Manchester ASHN and University Hospital Leeds; it will produce data urgently awaited by the NHS Commissioning Board. The provision of the 10 years of data for this specific project is critical to this analysis and subsequent publication which seeks to propose an optimum pathway for patients with AF at risk of a stroke	Harvey Walsh have received data on a monthly basis for the past 6 years. Harvey Walsh processes this data in the following ways: The raw flat HES files are downloaded from the SEFT server onto a drive on a secure dedicated in house server within Harvey Walsh offices in Runcorn. The data are then processed with costing algorithms applying tariffs to HRGs and all records are imported into two different databases within SQL Server - an archive database that has every requested column from the data files included, and a summary database that has a subset of columns for speed of processing. A verification process takes place on row count and then additional processing is undertaken applying the NHS cost algorithms and readmission calculations, the data is then ready to be made available for further use. The data are then split in two ways, for bespoke analysis by the SQL Analyst team and for the AXON 360 system. For bespoke analysis the refreshed data from the summary database is transferred via an internal secure network to 4 named analysts desktop SSD drives within SQL server. Only substantive employees of Harvey Walsh have access to the record level data in SQL and each of their machines is physically secure within a locked office and has 256 bit encryption and password protection. The employees who access record level HES data are logged on an access control register and are under contractual obligations with regards the safe and secure processing of sensitive data. Harvey Walsh is ISO27001/2013 compliant and all staff are fully trained on HES and undergo regular data security training. Prior to presentation to customers all outputs produced are checked by a Manager to ensure compliance to the HES Analysis user guide (i.e. aggregated data with small numbers suppressed) and all outputs have the stated purposes included and since 2015 have been recorded on a data output register. In addition a Privacy Impact Assessment (PIA) is now done on all HES analysis prior to commencement of a project. The AXON 360 system is processed as follows, the data are aggregated to a spell level (all related episodes made into a single spell row), and then this is further aggregated to an organisation and code level (ICD10, OPCS, HRG) when being rolled up into an OLAP cube. AXON 360 does not display individual episode or spell level data, the data are aggregated to an organisation or code level. The cube aggregates data at a diagnosis or organisation level will be accessed via the AXON application’s server code via a stored procedure on SQL Server. This allows another layer of security between the data and the AXON application layer. The data viewable in an AXON 360 report is pre-processed, aggregated and suppressed. AXON 360 is on a separate dedicated server on its own segregated network. This means that there is no access to the raw underlying HES data that is stored on a separate dedicated server within the Harvey Walsh facility. Harvey Walsh have also provided documented methodology to illustrate that the small number suppression they employ actually goes beyond the HES Analysis Guide providing further assurances. No record level data is provided to any third party organisation in any format. The only output will be aggregated data with small numbers suppressed as described and in line with the HES Analysis Guide. The data are not used to target sales individuals towards specific healthcare professionals and the data are not used within sales collateral used by sales/marketing teams this includes sales brochures, emails, direct mailing or advertising of pharmaceutical products. No record level data are transferred, stored or processed outside of the Harvey Walsh facility in Cheshire. The data already supplied and the data to be disseminated as described in this agreement will be used solely for the purposes outlined within this agreement. A data destruction certificate is required for data previously supplied that is not covered within this agreement.	Harvey Walsh undertake numerous projects utilizing HES on a yearly basis, approximately 100 distinct projects were completed in 2016/17. The outputs are varied and in different formats ranging from reports, charts, dashboards, budget impact models, health economic models, system dynamic models, health economic analysis, risk stratification, research papers and publications and AXON 360, see example at end of document. These outputs have achieved many useful things including changes in patient care, reduction in costs, uptake and monitoring of NHS policy. Harvey Walsh have a number of rolling contracts and Harvey Walsh expect to produce outputs on these up until at least 2019. Any output or analysis is ultimately delivered to improve patient care, pathways or healthcare services. Further details of outputs follow in the Benefits Section of this application. The retention of 10 year data will support the project looking at temporal trends in AF. The planned publication seeks to demonstrate how changes in the NHS have benefited this group of patients. Publications are planned for March 2018.	Harvey Walsh has held a data sharing agreement for over 8 years and has provided services to the NHS (NHS England, the NHS Board, AHSNs, trusts, CCGs, GP practices) and other healthcare related companies (Pharmaceutical and Device Companies, Patient Groups and Charities) during this time. Harvey Walsh have contracts which run through to 2019 and beyond and would expect to provide HES outputs through to this time. The solutions and projects that Harvey Walsh undertake have utilized cohort data from 2006 to date, and have been used for numerous different projects. The benefits that are provided from the analysis and insights of the HES data are direct and indirect to the patient and health and social care environment. Below are examples of direct benefits to patients and the NHS: 1: NHS England issued Clinical Commissioning Policy: Vagal Nerve Stimulation for Epilepsy April 2013 Reference: NHSCB/D04/P/d. Vagal Nerve Stimulation is used in refractory and drug resistant Epilepsy. Working with a device company and in collaboration with a Neurology Clinical Guidelines group analysis using HES has been undertaken to determine the: • Burden of Epilepsy and Variance across England demonstrating the patient pathways into specialist care • Modelled the capacity and potential requirements of surgical units so that patients who are suitable for treatment gain earlier access to treatment A health economic evaluation was undertaken on the healthcare utilisation pre and post implant to provide evidence to commissioners and NHS England on the benefits for patients as unplanned activity reduces as does cost to the NHS. This evaluation has been published Burke T, Hughes D, Forsey J, Bunker M, Bhattacharya D, Smithson WH, A Study of the Impact of VNS on Health Care Utilisation in England, SEIZURE: European Journal of Epilepsy (2015), http://dx.doi.org/10.1016/j.seizure.2015.11.002 2: Idiopathic Pulmonary Fibrosis Pathway Analysis: This project was undertaken with a commercial client for use in collaborative working with the NHS. Idiopathic pulmonary fibrosis (IPF) is a chronic and ultimately fatal disease characterized by a progressive decline in lung function. The term pulmonary fibrosis means scarring of lung tissue and is the cause of worsening dyspnoea (shortness of breath). Fibrosis is usually associated with a poor prognosis. In its earlier stages IPF mimics many routine chest conditions and requires specialist diagnostics to make a definitive diagnosis. Once diagnosed it is managed under specialist commissioning arrangements at key specialist centres such as the Royal Brompton. It’s quite possible for a patient to have many hospital admissions to local Hospital Trusts then specialist Chest Units before receiving a final diagnosis and referral to the appropriate Tertiary Specialist Centres. This makes determining a patient pathway difficult and the planning and commissioning of IPF services complicated. Tertiary referral trusts and their clinicians face challenges in planning and optimising capacity of IPF services due to the paucity of service data for this rare condition and the wide geographical distribution of patients. The IPF Pathway Analysis and Dashboard output supports the NHS to identify the current resource management of patients who may be identified as Idiopathic Pulmonary Fibrosis (IPF) patients in England. The dashboard provides a comprehensive Health Episode Statistic (HES) patient pathway and outcomes analysis from 2009 – 2014 across different co-morbidities and organisations. The data is displayed as reports and bespoke dashboards in order to support the NHS to plan for future patient and resource management requirements, for patients who may have IPF. In addition, by determining IPF records in 2014 to go back through the HES record for those patients to the first admission for a chest related condition and calculate two factors: • The Time from first Chest Condition Admission to diagnosis of IPF • To determine any patterns of Chest Conditions admissions that could be used as a marker for patients at risk of developing IPF to speed referral to the appropriate Tertiary Referral Specialist Trust for definitive diagnostics with aim of diagnosing and treating IPF earlier. The numbers of patients in England has been static around 5250 for the last 5 years accounting for approximately 7000 spells per year and costing on average £15m per year. For the first time it has been possible to say that the average time from first presentation with a chest condition to diagnosis with IPF is between 400 and 600 days. The top 5 chest conditions that account for the majority of hospital admissions prior to a diagnosis with IPF are: 1. Lobar Pneumonia Unspecified 2. Chronic Obstructive pulmonary disease with acute lower respiratory infection 3. Abnormal findings on diagnostic imaging of lung 4. Pneumonia unspecified 5. Interstitial Pulmonary disease unspecified Which presents an opportunity for the NHS to work to identify the types of patients with this pattern of admissions for definitive IPF diagnostics so reducing the time from presentation to diagnosis and treatment. Therefore directly improving outcomes for patients and reducing costs for the NHS. 3: Analysis of TURPS Patient Pathways for England: This work was undertaken with a commercial client to support NICE submission and to provide data to NHS England on innovative surgical techniques. Transurethral resection of the prostate (TURP) is a type of prostate surgery done to relieve moderate to severe urinary symptoms caused by an enlarged prostate, a condition known as benign prostatic hyperplasia (BPH). During TURP, a combined visual and surgical instrument (resectoscope) is inserted through the tip of the penis and into the (urethra). The urethra is surrounded by the prostate. Using the resectoscope, excess prostate tissue is removed that's blocking urine flow and increases the size urethra that allows the patient to empty their bladder. Current surgical approaches like TURP can leave permanent side effects such as urinary incontinence and erectile dysfunction. The aim of this project was to understand how many Patients had BPH, the number of TURPs undertaken for BPH and the complications associated with those TURPs and the long term impact of those complications on patient hospital admissions. This would indicate the real costs and patient impact of TURP as a baseline against which alternative less invasive procedures might be evaluated. A longitudinal analysis was performed on the HES Data Set for England and all records with a recorded diagnosis of BPH in 2013 who had within the same year undergone TURP. Harvey Walsh then identified subsequent admissions over the following year for the complications associated with TURP for those patients with a diagnosis of BPH also having a TURP in 2013. This data was presented nationally, and by CCG and hospital trust. Summary Post-operative complications add approximately 23% to the actual costs of performing TURP procedures and have a significant impact on patients’ lives. It is clear that clinically patients who undergo TURP and subsequently are admitted for a TURP complication will have that recorded in their notes. However, as the admissions for complication do not generally coincide with the spell during which the procedure is performed it may be very difficult for commissioners to see the impact in terms on subsequent hospitals admissions LOS and costs for patient undergoing TURP without investing scarce time and resource for data analysis for a procedure that is routine for this condition. Further as expected the peak in complication numbers & costs occurs in the year following procedure. However, in the 5 years following surgery many of the complications persist in reoccurring spells. TURP remains the standard procedure to relieve moderate to severe urinary symptoms caused by an enlarged prostate. In reviewing alternative procedures to TURP those that are less invasive but preclude the complications from TURP could have an impact on service cost and efficiency but potentially patient outcome not just in year but in the longer term. It was on the basis of this data analysis that the client was able to secure a meeting with the Office of Life Sciences to discuss complications of TURP as part of their remit to look for opportunities for innovation in patient care. This has now spread to NICE and there are on-going meetings to discuss the management pathways for these patients providing a direct benefit to patients by offering innovative procedures with less chance of complications whilst saving money for the NHS. 4. Renal Cancer Commissioning Insights Dashboard. (Commissioned by Pfizer) A quarterly refreshed insights tool that provides aggregated metrics to Providers and CCGs on: • Numbers of patients/growth/decline • Time to referral • Pathway of treatment • Healthcare utilisation • Variation and comparison The Pathways for Renal Cancer vary across the country. The objective of this project was to provide information and insights on current referral and management pathways for Renal Cancer to Commissioners and Clinicians. So that gaps in service provision and variation in care can be addressed by the development of local education and pathway support. This programme brings benefits to patients as it addresses service variation and supports improvement in quicker referral times to specialist centres for treatment and interventions 5. IBS Insights Tool (Commissioned by Allergan) A monthly refreshed dashboard that provides aggregated metrics to CCGs and Clinicians on the IBS Pathways showing: • Rates of scopes • Variation • Burden of IBS • Capacity mapping Irritable Bowel Syndrome (IBS) is often misdiagnosed and patients have unnecessary scopes which result in high costs for CCGs and capacity issues for Providers. The objective of this project was to show the current referral and management pathways for IBS to CCGs and provide the data and insights for service reviews and the development of local protocols to support appropriate referrals and reduce the numbers of referrals and thus reduce costs. This programme provides benefits to patients by reducing physical diagnostic scopes which are often not required and benefits to providers as capacity can be reduced and CCGs save money on scopes. 6. Atrial Fibrillation Insights Tool (Joint working Greater Manchester AHSN and Diatchi Sankyo) A monthly refreshed model with aggregated data (HES, QOF, Prescribing and GRASP) which maps the local impact and variation of AF across Greater Manchester Atrial Fibrillation is a ACS and QOF condition which is actively managed by GPs and CCGs. However significant numbers of patients still enter hospital as unplanned emergency admissions each month. The aim of this programme was to provide an in-depth analysis of AF across Greater Manchester, highlighting variance in care, uptake of NICE Guidance. Areas where performance and outcomes where not as good as expected were supported by educational and transformational programmes via the AHSN. This programme provides benefits to patients by improving how they are treated and aims to reduce admissions to hospital. This in turn benefits CCGs and Providers by reducing costs and unplanned admissions. 7. Saving Sight Campaign (commissioned by RNIB) Analysis of variation of Cataract Procedures across England by CCG to determine time to treatment. Originally commissioned in 2013, Harvey Walsh undertook a deep dive analysis on Cataract services in England to develop a publication Surgery Deferred Sight Denied Report 2013. This report showed significant variation in access to first and second eye surgery. This resulted in a number of interventions and educational support by the RNIB. In 2016 Harvey Walsh were asked by the RNIB to undertake a refreshed analysis based on the 2013 study. This analysis shows increases in treatment and a decrease in the variation of care with over 100,000 additional procedures taking place since the initial study. This programme has produced improvements in time to treatment and the treatment of second eye blindness resulting in benefits for patients and the wider healthcare arena. 8. Impact of Vaccination on the rates of Pneumococcal Disease in England since 2006 (Commissioned by Pfizer) Analysis of HES data to produce aggregated national analysis on the rates of Pneumococcal Disease over 10 years The complications of Pneumonia can lead to devastating outcomes such as death, meningitis and sepsis. The objective of this analysis was to determine whether vaccines have had an impact on these complications and the outputs will be used to provide evidence to NICE and other NHS bodies to support their decisions on future vaccine programmes. This work has provided benefits to patients by providing outputs to support further vaccine programmes which will result in less people developing the consequence of Pneumonia Infections.
HARVEY WALSH LTD	HARVEY WALSH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Harvey Walsh is an IT solutions, NHS Data and Health Informatics Consultancy which provides services to the NHS, Academic Health Science Networks (AHSNs) Pharmaceutical and Device Industry, Patient Groups and Healthcare Charities. Harvey Walsh uses pseudonymised, monthly refreshed HES data to undertake analysis, develop services and provide solutions to support commissioning, analytical support and outcomes analysis for NHS organisations, which include NHS England, the NHS Board, GP Practices, GP Federations, CCGs, Regional Teams, Academic Health Science Networks, Health and Well-Being Boards, Provider Trusts, Ambulance Trusts, CSUs and Sustainability and Transformation Partnerships (STPs). The services and solutions provided include analysis and outputs for the purposes of informing commissioners, healthcare professionals and clinical networks on uptake on innovation national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. Harvey Walsh also utilise these HES data to provide services to commercial organisations within the pharmaceutical, medical device industry, patient organisations, healthcare charities and AHSNs. These organisations use the outputs and insights provided by Harvey Walsh to work collaboratively with NHS organisations to promote health and improve the well-being of patients. The use of the data supports the development of innovative solutions and service improvement, to track outcomes and provide the real world evidence as required by the NHS, NICE, Monitor and NHS England. The aim of which is to improve patient care and support enhanced access to improved services and innovative solutions. In addition these clients use these outputs for the purposes of providing supporting information required by the NHS for business cases, epidemiological research, pathway analysis, burden of disease analysis, health economic research, predictive analytical modelling, NICE submissions and quality and outcome analysis. The outputs of which are shared directly or indirectly with the NHS to support improvements in patient care. All outputs are aggregated with small numbers suppressed in line with the HES analysis guide. In addition to this analytical work, Harvey Walsh also provide a tool called AXON 360. AXON 360 is an online tool that is hosted within the Harvey Walsh offices in a secure server room from a dedicated network. AXON 360 incorporates aggregated HES, QOF and primary care prescribing data, this tool allows users to analyse healthcare data, derive a range of insights and produce reports to support improvements in patient care and provide insights and evidence for commissioners and healthcare professionals. Data is also analysed for the purposes of informing commissioners, clinicians and clinical networks on uptake on innovation, national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. All data is aggregated and small numbers are suppressed, including secondary suppression as per the HES analysis guide. The outputs that utilize the HES data are provided directly to the NHS and AHSNs or will be provided indirectly to the NHS via the pharmaceutical and device companies and AHSNs using bespoke outputs, reports, dashboards, research papers and via a tool called AXON 360 produced by Harvey Walsh. All of the outputs from the analysis of HES are used directly or indirectly for the provision or promotion of health and improved patient care and are not wholly commercial. Each purpose within this application (including the Axon tool) other than one specific project stated here (AF project) are only permitted to use 5 years of HES data. An AF project is already underway and is looking at temporal trends over the past 10 years. The project is a collaboration between Harvey Walsh, Greater Manchester ASHN and University Hospital Leeds; it will produce data urgently awaited by the NHS Commissioning Board. The provision of the 10 years of data for this specific project is critical to this analysis and subsequent publication which seeks to propose an optimum pathway for patients with AF at risk of a stroke	Harvey Walsh have received data on a monthly basis for the past 6 years. Harvey Walsh processes this data in the following ways: The raw flat HES files are downloaded from the SEFT server onto a drive on a secure dedicated in house server within Harvey Walsh offices in Runcorn. The data are then processed with costing algorithms applying tariffs to HRGs and all records are imported into two different databases within SQL Server - an archive database that has every requested column from the data files included, and a summary database that has a subset of columns for speed of processing. A verification process takes place on row count and then additional processing is undertaken applying the NHS cost algorithms and readmission calculations, the data is then ready to be made available for further use. The data are then split in two ways, for bespoke analysis by the SQL Analyst team and for the AXON 360 system. For bespoke analysis the refreshed data from the summary database is transferred via an internal secure network to 4 named analysts desktop SSD drives within SQL server. Only substantive employees of Harvey Walsh have access to the record level data in SQL and each of their machines is physically secure within a locked office and has 256 bit encryption and password protection. The employees who access record level HES data are logged on an access control register and are under contractual obligations with regards the safe and secure processing of sensitive data. Harvey Walsh is ISO27001/2013 compliant and all staff are fully trained on HES and undergo regular data security training. Prior to presentation to customers all outputs produced are checked by a Manager to ensure compliance to the HES Analysis user guide (i.e. aggregated data with small numbers suppressed) and all outputs have the stated purposes included and since 2015 have been recorded on a data output register. In addition a Privacy Impact Assessment (PIA) is now done on all HES analysis prior to commencement of a project. The AXON 360 system is processed as follows, the data are aggregated to a spell level (all related episodes made into a single spell row), and then this is further aggregated to an organisation and code level (ICD10, OPCS, HRG) when being rolled up into an OLAP cube. AXON 360 does not display individual episode or spell level data, the data are aggregated to an organisation or code level. The cube aggregates data at a diagnosis or organisation level will be accessed via the AXON application’s server code via a stored procedure on SQL Server. This allows another layer of security between the data and the AXON application layer. The data viewable in an AXON 360 report is pre-processed, aggregated and suppressed. AXON 360 is on a separate dedicated server on its own segregated network. This means that there is no access to the raw underlying HES data that is stored on a separate dedicated server within the Harvey Walsh facility. Harvey Walsh have also provided documented methodology to illustrate that the small number suppression they employ actually goes beyond the HES Analysis Guide providing further assurances. No record level data is provided to any third party organisation in any format. The only output will be aggregated data with small numbers suppressed as described and in line with the HES Analysis Guide. The data are not used to target sales individuals towards specific healthcare professionals and the data are not used within sales collateral used by sales/marketing teams this includes sales brochures, emails, direct mailing or advertising of pharmaceutical products. No record level data are transferred, stored or processed outside of the Harvey Walsh facility in Cheshire. The data already supplied and the data to be disseminated as described in this agreement will be used solely for the purposes outlined within this agreement. A data destruction certificate is required for data previously supplied that is not covered within this agreement.	Harvey Walsh undertake numerous projects utilizing HES on a yearly basis, approximately 100 distinct projects were completed in 2016/17. The outputs are varied and in different formats ranging from reports, charts, dashboards, budget impact models, health economic models, system dynamic models, health economic analysis, risk stratification, research papers and publications and AXON 360, see example at end of document. These outputs have achieved many useful things including changes in patient care, reduction in costs, uptake and monitoring of NHS policy. Harvey Walsh have a number of rolling contracts and Harvey Walsh expect to produce outputs on these up until at least 2019. Any output or analysis is ultimately delivered to improve patient care, pathways or healthcare services. Further details of outputs follow in the Benefits Section of this application. The retention of 10 year data will support the project looking at temporal trends in AF. The planned publication seeks to demonstrate how changes in the NHS have benefited this group of patients. Publications are planned for March 2018.	Harvey Walsh has held a data sharing agreement for over 8 years and has provided services to the NHS (NHS England, the NHS Board, AHSNs, trusts, CCGs, GP practices) and other healthcare related companies (Pharmaceutical and Device Companies, Patient Groups and Charities) during this time. Harvey Walsh have contracts which run through to 2019 and beyond and would expect to provide HES outputs through to this time. The solutions and projects that Harvey Walsh undertake have utilized cohort data from 2006 to date, and have been used for numerous different projects. The benefits that are provided from the analysis and insights of the HES data are direct and indirect to the patient and health and social care environment. Below are examples of direct benefits to patients and the NHS: 1: NHS England issued Clinical Commissioning Policy: Vagal Nerve Stimulation for Epilepsy April 2013 Reference: NHSCB/D04/P/d. Vagal Nerve Stimulation is used in refractory and drug resistant Epilepsy. Working with a device company and in collaboration with a Neurology Clinical Guidelines group analysis using HES has been undertaken to determine the: • Burden of Epilepsy and Variance across England demonstrating the patient pathways into specialist care • Modelled the capacity and potential requirements of surgical units so that patients who are suitable for treatment gain earlier access to treatment A health economic evaluation was undertaken on the healthcare utilisation pre and post implant to provide evidence to commissioners and NHS England on the benefits for patients as unplanned activity reduces as does cost to the NHS. This evaluation has been published Burke T, Hughes D, Forsey J, Bunker M, Bhattacharya D, Smithson WH, A Study of the Impact of VNS on Health Care Utilisation in England, SEIZURE: European Journal of Epilepsy (2015), http://dx.doi.org/10.1016/j.seizure.2015.11.002 2: Idiopathic Pulmonary Fibrosis Pathway Analysis: This project was undertaken with a commercial client for use in collaborative working with the NHS. Idiopathic pulmonary fibrosis (IPF) is a chronic and ultimately fatal disease characterized by a progressive decline in lung function. The term pulmonary fibrosis means scarring of lung tissue and is the cause of worsening dyspnoea (shortness of breath). Fibrosis is usually associated with a poor prognosis. In its earlier stages IPF mimics many routine chest conditions and requires specialist diagnostics to make a definitive diagnosis. Once diagnosed it is managed under specialist commissioning arrangements at key specialist centres such as the Royal Brompton. It’s quite possible for a patient to have many hospital admissions to local Hospital Trusts then specialist Chest Units before receiving a final diagnosis and referral to the appropriate Tertiary Specialist Centres. This makes determining a patient pathway difficult and the planning and commissioning of IPF services complicated. Tertiary referral trusts and their clinicians face challenges in planning and optimising capacity of IPF services due to the paucity of service data for this rare condition and the wide geographical distribution of patients. The IPF Pathway Analysis and Dashboard output supports the NHS to identify the current resource management of patients who may be identified as Idiopathic Pulmonary Fibrosis (IPF) patients in England. The dashboard provides a comprehensive Health Episode Statistic (HES) patient pathway and outcomes analysis from 2009 – 2014 across different co-morbidities and organisations. The data is displayed as reports and bespoke dashboards in order to support the NHS to plan for future patient and resource management requirements, for patients who may have IPF. In addition, by determining IPF records in 2014 to go back through the HES record for those patients to the first admission for a chest related condition and calculate two factors: • The Time from first Chest Condition Admission to diagnosis of IPF • To determine any patterns of Chest Conditions admissions that could be used as a marker for patients at risk of developing IPF to speed referral to the appropriate Tertiary Referral Specialist Trust for definitive diagnostics with aim of diagnosing and treating IPF earlier. The numbers of patients in England has been static around 5250 for the last 5 years accounting for approximately 7000 spells per year and costing on average £15m per year. For the first time it has been possible to say that the average time from first presentation with a chest condition to diagnosis with IPF is between 400 and 600 days. The top 5 chest conditions that account for the majority of hospital admissions prior to a diagnosis with IPF are: 1. Lobar Pneumonia Unspecified 2. Chronic Obstructive pulmonary disease with acute lower respiratory infection 3. Abnormal findings on diagnostic imaging of lung 4. Pneumonia unspecified 5. Interstitial Pulmonary disease unspecified Which presents an opportunity for the NHS to work to identify the types of patients with this pattern of admissions for definitive IPF diagnostics so reducing the time from presentation to diagnosis and treatment. Therefore directly improving outcomes for patients and reducing costs for the NHS. 3: Analysis of TURPS Patient Pathways for England: This work was undertaken with a commercial client to support NICE submission and to provide data to NHS England on innovative surgical techniques. Transurethral resection of the prostate (TURP) is a type of prostate surgery done to relieve moderate to severe urinary symptoms caused by an enlarged prostate, a condition known as benign prostatic hyperplasia (BPH). During TURP, a combined visual and surgical instrument (resectoscope) is inserted through the tip of the penis and into the (urethra). The urethra is surrounded by the prostate. Using the resectoscope, excess prostate tissue is removed that's blocking urine flow and increases the size urethra that allows the patient to empty their bladder. Current surgical approaches like TURP can leave permanent side effects such as urinary incontinence and erectile dysfunction. The aim of this project was to understand how many Patients had BPH, the number of TURPs undertaken for BPH and the complications associated with those TURPs and the long term impact of those complications on patient hospital admissions. This would indicate the real costs and patient impact of TURP as a baseline against which alternative less invasive procedures might be evaluated. A longitudinal analysis was performed on the HES Data Set for England and all records with a recorded diagnosis of BPH in 2013 who had within the same year undergone TURP. Harvey Walsh then identified subsequent admissions over the following year for the complications associated with TURP for those patients with a diagnosis of BPH also having a TURP in 2013. This data was presented nationally, and by CCG and hospital trust. Summary Post-operative complications add approximately 23% to the actual costs of performing TURP procedures and have a significant impact on patients’ lives. It is clear that clinically patients who undergo TURP and subsequently are admitted for a TURP complication will have that recorded in their notes. However, as the admissions for complication do not generally coincide with the spell during which the procedure is performed it may be very difficult for commissioners to see the impact in terms on subsequent hospitals admissions LOS and costs for patient undergoing TURP without investing scarce time and resource for data analysis for a procedure that is routine for this condition. Further as expected the peak in complication numbers & costs occurs in the year following procedure. However, in the 5 years following surgery many of the complications persist in reoccurring spells. TURP remains the standard procedure to relieve moderate to severe urinary symptoms caused by an enlarged prostate. In reviewing alternative procedures to TURP those that are less invasive but preclude the complications from TURP could have an impact on service cost and efficiency but potentially patient outcome not just in year but in the longer term. It was on the basis of this data analysis that the client was able to secure a meeting with the Office of Life Sciences to discuss complications of TURP as part of their remit to look for opportunities for innovation in patient care. This has now spread to NICE and there are on-going meetings to discuss the management pathways for these patients providing a direct benefit to patients by offering innovative procedures with less chance of complications whilst saving money for the NHS. 4. Renal Cancer Commissioning Insights Dashboard. (Commissioned by Pfizer) A quarterly refreshed insights tool that provides aggregated metrics to Providers and CCGs on: • Numbers of patients/growth/decline • Time to referral • Pathway of treatment • Healthcare utilisation • Variation and comparison The Pathways for Renal Cancer vary across the country. The objective of this project was to provide information and insights on current referral and management pathways for Renal Cancer to Commissioners and Clinicians. So that gaps in service provision and variation in care can be addressed by the development of local education and pathway support. This programme brings benefits to patients as it addresses service variation and supports improvement in quicker referral times to specialist centres for treatment and interventions 5. IBS Insights Tool (Commissioned by Allergan) A monthly refreshed dashboard that provides aggregated metrics to CCGs and Clinicians on the IBS Pathways showing: • Rates of scopes • Variation • Burden of IBS • Capacity mapping Irritable Bowel Syndrome (IBS) is often misdiagnosed and patients have unnecessary scopes which result in high costs for CCGs and capacity issues for Providers. The objective of this project was to show the current referral and management pathways for IBS to CCGs and provide the data and insights for service reviews and the development of local protocols to support appropriate referrals and reduce the numbers of referrals and thus reduce costs. This programme provides benefits to patients by reducing physical diagnostic scopes which are often not required and benefits to providers as capacity can be reduced and CCGs save money on scopes. 6. Atrial Fibrillation Insights Tool (Joint working Greater Manchester AHSN and Diatchi Sankyo) A monthly refreshed model with aggregated data (HES, QOF, Prescribing and GRASP) which maps the local impact and variation of AF across Greater Manchester Atrial Fibrillation is a ACS and QOF condition which is actively managed by GPs and CCGs. However significant numbers of patients still enter hospital as unplanned emergency admissions each month. The aim of this programme was to provide an in-depth analysis of AF across Greater Manchester, highlighting variance in care, uptake of NICE Guidance. Areas where performance and outcomes where not as good as expected were supported by educational and transformational programmes via the AHSN. This programme provides benefits to patients by improving how they are treated and aims to reduce admissions to hospital. This in turn benefits CCGs and Providers by reducing costs and unplanned admissions. 7. Saving Sight Campaign (commissioned by RNIB) Analysis of variation of Cataract Procedures across England by CCG to determine time to treatment. Originally commissioned in 2013, Harvey Walsh undertook a deep dive analysis on Cataract services in England to develop a publication Surgery Deferred Sight Denied Report 2013. This report showed significant variation in access to first and second eye surgery. This resulted in a number of interventions and educational support by the RNIB. In 2016 Harvey Walsh were asked by the RNIB to undertake a refreshed analysis based on the 2013 study. This analysis shows increases in treatment and a decrease in the variation of care with over 100,000 additional procedures taking place since the initial study. This programme has produced improvements in time to treatment and the treatment of second eye blindness resulting in benefits for patients and the wider healthcare arena. 8. Impact of Vaccination on the rates of Pneumococcal Disease in England since 2006 (Commissioned by Pfizer) Analysis of HES data to produce aggregated national analysis on the rates of Pneumococcal Disease over 10 years The complications of Pneumonia can lead to devastating outcomes such as death, meningitis and sepsis. The objective of this analysis was to determine whether vaccines have had an impact on these complications and the outputs will be used to provide evidence to NICE and other NHS bodies to support their decisions on future vaccine programmes. This work has provided benefits to patients by providing outputs to support further vaccine programmes which will result in less people developing the consequence of Pneumonia Infections.
HARVEY WALSH LTD	HARVEY WALSH LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Harvey Walsh is an IT solutions, NHS Data and Health Informatics Consultancy which provides services to the NHS, Academic Health Science Networks (AHSNs) Pharmaceutical and Device Industry, Patient Groups and Healthcare Charities. Harvey Walsh uses pseudonymised, monthly refreshed HES data to undertake analysis, develop services and provide solutions to support commissioning, analytical support and outcomes analysis for NHS organisations, which include NHS England, the NHS Board, GP Practices, GP Federations, CCGs, Regional Teams, Academic Health Science Networks, Health and Well-Being Boards, Provider Trusts, Ambulance Trusts, CSUs and Sustainability and Transformation Partnerships (STPs). The services and solutions provided include analysis and outputs for the purposes of informing commissioners, healthcare professionals and clinical networks on uptake on innovation national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. Harvey Walsh also utilise these HES data to provide services to commercial organisations within the pharmaceutical, medical device industry, patient organisations, healthcare charities and AHSNs. These organisations use the outputs and insights provided by Harvey Walsh to work collaboratively with NHS organisations to promote health and improve the well-being of patients. The use of the data supports the development of innovative solutions and service improvement, to track outcomes and provide the real world evidence as required by the NHS, NICE, Monitor and NHS England. The aim of which is to improve patient care and support enhanced access to improved services and innovative solutions. In addition these clients use these outputs for the purposes of providing supporting information required by the NHS for business cases, epidemiological research, pathway analysis, burden of disease analysis, health economic research, predictive analytical modelling, NICE submissions and quality and outcome analysis. The outputs of which are shared directly or indirectly with the NHS to support improvements in patient care. All outputs are aggregated with small numbers suppressed in line with the HES analysis guide. In addition to this analytical work, Harvey Walsh also provide a tool called AXON 360. AXON 360 is an online tool that is hosted within the Harvey Walsh offices in a secure server room from a dedicated network. AXON 360 incorporates aggregated HES, QOF and primary care prescribing data, this tool allows users to analyse healthcare data, derive a range of insights and produce reports to support improvements in patient care and provide insights and evidence for commissioners and healthcare professionals. Data is also analysed for the purposes of informing commissioners, clinicians and clinical networks on uptake on innovation, national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. All data is aggregated and small numbers are suppressed, including secondary suppression as per the HES analysis guide. The outputs that utilize the HES data are provided directly to the NHS and AHSNs or will be provided indirectly to the NHS via the pharmaceutical and device companies and AHSNs using bespoke outputs, reports, dashboards, research papers and via a tool called AXON 360 produced by Harvey Walsh. All of the outputs from the analysis of HES are used directly or indirectly for the provision or promotion of health and improved patient care and are not wholly commercial. Each purpose within this application (including the Axon tool) other than one specific project stated here (AF project) are only permitted to use 5 years of HES data. An AF project is already underway and is looking at temporal trends over the past 10 years. The project is a collaboration between Harvey Walsh, Greater Manchester ASHN and University Hospital Leeds; it will produce data urgently awaited by the NHS Commissioning Board. The provision of the 10 years of data for this specific project is critical to this analysis and subsequent publication which seeks to propose an optimum pathway for patients with AF at risk of a stroke	Harvey Walsh have received data on a monthly basis for the past 6 years. Harvey Walsh processes this data in the following ways: The raw flat HES files are downloaded from the SEFT server onto a drive on a secure dedicated in house server within Harvey Walsh offices in Runcorn. The data are then processed with costing algorithms applying tariffs to HRGs and all records are imported into two different databases within SQL Server - an archive database that has every requested column from the data files included, and a summary database that has a subset of columns for speed of processing. A verification process takes place on row count and then additional processing is undertaken applying the NHS cost algorithms and readmission calculations, the data is then ready to be made available for further use. The data are then split in two ways, for bespoke analysis by the SQL Analyst team and for the AXON 360 system. For bespoke analysis the refreshed data from the summary database is transferred via an internal secure network to 4 named analysts desktop SSD drives within SQL server. Only substantive employees of Harvey Walsh have access to the record level data in SQL and each of their machines is physically secure within a locked office and has 256 bit encryption and password protection. The employees who access record level HES data are logged on an access control register and are under contractual obligations with regards the safe and secure processing of sensitive data. Harvey Walsh is ISO27001/2013 compliant and all staff are fully trained on HES and undergo regular data security training. Prior to presentation to customers all outputs produced are checked by a Manager to ensure compliance to the HES Analysis user guide (i.e. aggregated data with small numbers suppressed) and all outputs have the stated purposes included and since 2015 have been recorded on a data output register. In addition a Privacy Impact Assessment (PIA) is now done on all HES analysis prior to commencement of a project. The AXON 360 system is processed as follows, the data are aggregated to a spell level (all related episodes made into a single spell row), and then this is further aggregated to an organisation and code level (ICD10, OPCS, HRG) when being rolled up into an OLAP cube. AXON 360 does not display individual episode or spell level data, the data are aggregated to an organisation or code level. The cube aggregates data at a diagnosis or organisation level will be accessed via the AXON application’s server code via a stored procedure on SQL Server. This allows another layer of security between the data and the AXON application layer. The data viewable in an AXON 360 report is pre-processed, aggregated and suppressed. AXON 360 is on a separate dedicated server on its own segregated network. This means that there is no access to the raw underlying HES data that is stored on a separate dedicated server within the Harvey Walsh facility. Harvey Walsh have also provided documented methodology to illustrate that the small number suppression they employ actually goes beyond the HES Analysis Guide providing further assurances. No record level data is provided to any third party organisation in any format. The only output will be aggregated data with small numbers suppressed as described and in line with the HES Analysis Guide. The data are not used to target sales individuals towards specific healthcare professionals and the data are not used within sales collateral used by sales/marketing teams this includes sales brochures, emails, direct mailing or advertising of pharmaceutical products. No record level data are transferred, stored or processed outside of the Harvey Walsh facility in Cheshire. The data already supplied and the data to be disseminated as described in this agreement will be used solely for the purposes outlined within this agreement. A data destruction certificate is required for data previously supplied that is not covered within this agreement.	Harvey Walsh undertake numerous projects utilizing HES on a yearly basis, approximately 100 distinct projects were completed in 2016/17. The outputs are varied and in different formats ranging from reports, charts, dashboards, budget impact models, health economic models, system dynamic models, health economic analysis, risk stratification, research papers and publications and AXON 360, see example at end of document. These outputs have achieved many useful things including changes in patient care, reduction in costs, uptake and monitoring of NHS policy. Harvey Walsh have a number of rolling contracts and Harvey Walsh expect to produce outputs on these up until at least 2019. Any output or analysis is ultimately delivered to improve patient care, pathways or healthcare services. Further details of outputs follow in the Benefits Section of this application. The retention of 10 year data will support the project looking at temporal trends in AF. The planned publication seeks to demonstrate how changes in the NHS have benefited this group of patients. Publications are planned for March 2018.	Harvey Walsh has held a data sharing agreement for over 8 years and has provided services to the NHS (NHS England, the NHS Board, AHSNs, trusts, CCGs, GP practices) and other healthcare related companies (Pharmaceutical and Device Companies, Patient Groups and Charities) during this time. Harvey Walsh have contracts which run through to 2019 and beyond and would expect to provide HES outputs through to this time. The solutions and projects that Harvey Walsh undertake have utilized cohort data from 2006 to date, and have been used for numerous different projects. The benefits that are provided from the analysis and insights of the HES data are direct and indirect to the patient and health and social care environment. Below are examples of direct benefits to patients and the NHS: 1: NHS England issued Clinical Commissioning Policy: Vagal Nerve Stimulation for Epilepsy April 2013 Reference: NHSCB/D04/P/d. Vagal Nerve Stimulation is used in refractory and drug resistant Epilepsy. Working with a device company and in collaboration with a Neurology Clinical Guidelines group analysis using HES has been undertaken to determine the: • Burden of Epilepsy and Variance across England demonstrating the patient pathways into specialist care • Modelled the capacity and potential requirements of surgical units so that patients who are suitable for treatment gain earlier access to treatment A health economic evaluation was undertaken on the healthcare utilisation pre and post implant to provide evidence to commissioners and NHS England on the benefits for patients as unplanned activity reduces as does cost to the NHS. This evaluation has been published Burke T, Hughes D, Forsey J, Bunker M, Bhattacharya D, Smithson WH, A Study of the Impact of VNS on Health Care Utilisation in England, SEIZURE: European Journal of Epilepsy (2015), http://dx.doi.org/10.1016/j.seizure.2015.11.002 2: Idiopathic Pulmonary Fibrosis Pathway Analysis: This project was undertaken with a commercial client for use in collaborative working with the NHS. Idiopathic pulmonary fibrosis (IPF) is a chronic and ultimately fatal disease characterized by a progressive decline in lung function. The term pulmonary fibrosis means scarring of lung tissue and is the cause of worsening dyspnoea (shortness of breath). Fibrosis is usually associated with a poor prognosis. In its earlier stages IPF mimics many routine chest conditions and requires specialist diagnostics to make a definitive diagnosis. Once diagnosed it is managed under specialist commissioning arrangements at key specialist centres such as the Royal Brompton. It’s quite possible for a patient to have many hospital admissions to local Hospital Trusts then specialist Chest Units before receiving a final diagnosis and referral to the appropriate Tertiary Specialist Centres. This makes determining a patient pathway difficult and the planning and commissioning of IPF services complicated. Tertiary referral trusts and their clinicians face challenges in planning and optimising capacity of IPF services due to the paucity of service data for this rare condition and the wide geographical distribution of patients. The IPF Pathway Analysis and Dashboard output supports the NHS to identify the current resource management of patients who may be identified as Idiopathic Pulmonary Fibrosis (IPF) patients in England. The dashboard provides a comprehensive Health Episode Statistic (HES) patient pathway and outcomes analysis from 2009 – 2014 across different co-morbidities and organisations. The data is displayed as reports and bespoke dashboards in order to support the NHS to plan for future patient and resource management requirements, for patients who may have IPF. In addition, by determining IPF records in 2014 to go back through the HES record for those patients to the first admission for a chest related condition and calculate two factors: • The Time from first Chest Condition Admission to diagnosis of IPF • To determine any patterns of Chest Conditions admissions that could be used as a marker for patients at risk of developing IPF to speed referral to the appropriate Tertiary Referral Specialist Trust for definitive diagnostics with aim of diagnosing and treating IPF earlier. The numbers of patients in England has been static around 5250 for the last 5 years accounting for approximately 7000 spells per year and costing on average £15m per year. For the first time it has been possible to say that the average time from first presentation with a chest condition to diagnosis with IPF is between 400 and 600 days. The top 5 chest conditions that account for the majority of hospital admissions prior to a diagnosis with IPF are: 1. Lobar Pneumonia Unspecified 2. Chronic Obstructive pulmonary disease with acute lower respiratory infection 3. Abnormal findings on diagnostic imaging of lung 4. Pneumonia unspecified 5. Interstitial Pulmonary disease unspecified Which presents an opportunity for the NHS to work to identify the types of patients with this pattern of admissions for definitive IPF diagnostics so reducing the time from presentation to diagnosis and treatment. Therefore directly improving outcomes for patients and reducing costs for the NHS. 3: Analysis of TURPS Patient Pathways for England: This work was undertaken with a commercial client to support NICE submission and to provide data to NHS England on innovative surgical techniques. Transurethral resection of the prostate (TURP) is a type of prostate surgery done to relieve moderate to severe urinary symptoms caused by an enlarged prostate, a condition known as benign prostatic hyperplasia (BPH). During TURP, a combined visual and surgical instrument (resectoscope) is inserted through the tip of the penis and into the (urethra). The urethra is surrounded by the prostate. Using the resectoscope, excess prostate tissue is removed that's blocking urine flow and increases the size urethra that allows the patient to empty their bladder. Current surgical approaches like TURP can leave permanent side effects such as urinary incontinence and erectile dysfunction. The aim of this project was to understand how many Patients had BPH, the number of TURPs undertaken for BPH and the complications associated with those TURPs and the long term impact of those complications on patient hospital admissions. This would indicate the real costs and patient impact of TURP as a baseline against which alternative less invasive procedures might be evaluated. A longitudinal analysis was performed on the HES Data Set for England and all records with a recorded diagnosis of BPH in 2013 who had within the same year undergone TURP. Harvey Walsh then identified subsequent admissions over the following year for the complications associated with TURP for those patients with a diagnosis of BPH also having a TURP in 2013. This data was presented nationally, and by CCG and hospital trust. Summary Post-operative complications add approximately 23% to the actual costs of performing TURP procedures and have a significant impact on patients’ lives. It is clear that clinically patients who undergo TURP and subsequently are admitted for a TURP complication will have that recorded in their notes. However, as the admissions for complication do not generally coincide with the spell during which the procedure is performed it may be very difficult for commissioners to see the impact in terms on subsequent hospitals admissions LOS and costs for patient undergoing TURP without investing scarce time and resource for data analysis for a procedure that is routine for this condition. Further as expected the peak in complication numbers & costs occurs in the year following procedure. However, in the 5 years following surgery many of the complications persist in reoccurring spells. TURP remains the standard procedure to relieve moderate to severe urinary symptoms caused by an enlarged prostate. In reviewing alternative procedures to TURP those that are less invasive but preclude the complications from TURP could have an impact on service cost and efficiency but potentially patient outcome not just in year but in the longer term. It was on the basis of this data analysis that the client was able to secure a meeting with the Office of Life Sciences to discuss complications of TURP as part of their remit to look for opportunities for innovation in patient care. This has now spread to NICE and there are on-going meetings to discuss the management pathways for these patients providing a direct benefit to patients by offering innovative procedures with less chance of complications whilst saving money for the NHS. 4. Renal Cancer Commissioning Insights Dashboard. (Commissioned by Pfizer) A quarterly refreshed insights tool that provides aggregated metrics to Providers and CCGs on: • Numbers of patients/growth/decline • Time to referral • Pathway of treatment • Healthcare utilisation • Variation and comparison The Pathways for Renal Cancer vary across the country. The objective of this project was to provide information and insights on current referral and management pathways for Renal Cancer to Commissioners and Clinicians. So that gaps in service provision and variation in care can be addressed by the development of local education and pathway support. This programme brings benefits to patients as it addresses service variation and supports improvement in quicker referral times to specialist centres for treatment and interventions 5. IBS Insights Tool (Commissioned by Allergan) A monthly refreshed dashboard that provides aggregated metrics to CCGs and Clinicians on the IBS Pathways showing: • Rates of scopes • Variation • Burden of IBS • Capacity mapping Irritable Bowel Syndrome (IBS) is often misdiagnosed and patients have unnecessary scopes which result in high costs for CCGs and capacity issues for Providers. The objective of this project was to show the current referral and management pathways for IBS to CCGs and provide the data and insights for service reviews and the development of local protocols to support appropriate referrals and reduce the numbers of referrals and thus reduce costs. This programme provides benefits to patients by reducing physical diagnostic scopes which are often not required and benefits to providers as capacity can be reduced and CCGs save money on scopes. 6. Atrial Fibrillation Insights Tool (Joint working Greater Manchester AHSN and Diatchi Sankyo) A monthly refreshed model with aggregated data (HES, QOF, Prescribing and GRASP) which maps the local impact and variation of AF across Greater Manchester Atrial Fibrillation is a ACS and QOF condition which is actively managed by GPs and CCGs. However significant numbers of patients still enter hospital as unplanned emergency admissions each month. The aim of this programme was to provide an in-depth analysis of AF across Greater Manchester, highlighting variance in care, uptake of NICE Guidance. Areas where performance and outcomes where not as good as expected were supported by educational and transformational programmes via the AHSN. This programme provides benefits to patients by improving how they are treated and aims to reduce admissions to hospital. This in turn benefits CCGs and Providers by reducing costs and unplanned admissions. 7. Saving Sight Campaign (commissioned by RNIB) Analysis of variation of Cataract Procedures across England by CCG to determine time to treatment. Originally commissioned in 2013, Harvey Walsh undertook a deep dive analysis on Cataract services in England to develop a publication Surgery Deferred Sight Denied Report 2013. This report showed significant variation in access to first and second eye surgery. This resulted in a number of interventions and educational support by the RNIB. In 2016 Harvey Walsh were asked by the RNIB to undertake a refreshed analysis based on the 2013 study. This analysis shows increases in treatment and a decrease in the variation of care with over 100,000 additional procedures taking place since the initial study. This programme has produced improvements in time to treatment and the treatment of second eye blindness resulting in benefits for patients and the wider healthcare arena. 8. Impact of Vaccination on the rates of Pneumococcal Disease in England since 2006 (Commissioned by Pfizer) Analysis of HES data to produce aggregated national analysis on the rates of Pneumococcal Disease over 10 years The complications of Pneumonia can lead to devastating outcomes such as death, meningitis and sepsis. The objective of this analysis was to determine whether vaccines have had an impact on these complications and the outputs will be used to provide evidence to NICE and other NHS bodies to support their decisions on future vaccine programmes. This work has provided benefits to patients by providing outputs to support further vaccine programmes which will result in less people developing the consequence of Pneumonia Infections.
HARVEY WALSH LTD	HARVEY WALSH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Harvey Walsh is an IT solutions, NHS Data and Health Informatics Consultancy which provides services to the NHS, Academic Health Science Networks (AHSNs) Pharmaceutical and Device Industry, Patient Groups and Healthcare Charities. Harvey Walsh uses pseudonymised, monthly refreshed HES data to undertake analysis, develop services and provide solutions to support commissioning, analytical support and outcomes analysis for NHS organisations, which include NHS England, the NHS Board, GP Practices, GP Federations, CCGs, Regional Teams, Academic Health Science Networks, Health and Well-Being Boards, Provider Trusts, Ambulance Trusts, CSUs and Sustainability and Transformation Partnerships (STPs). The services and solutions provided include analysis and outputs for the purposes of informing commissioners, healthcare professionals and clinical networks on uptake on innovation national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. Harvey Walsh also utilise these HES data to provide services to commercial organisations within the pharmaceutical, medical device industry, patient organisations, healthcare charities and AHSNs. These organisations use the outputs and insights provided by Harvey Walsh to work collaboratively with NHS organisations to promote health and improve the well-being of patients. The use of the data supports the development of innovative solutions and service improvement, to track outcomes and provide the real world evidence as required by the NHS, NICE, Monitor and NHS England. The aim of which is to improve patient care and support enhanced access to improved services and innovative solutions. In addition these clients use these outputs for the purposes of providing supporting information required by the NHS for business cases, epidemiological research, pathway analysis, burden of disease analysis, health economic research, predictive analytical modelling, NICE submissions and quality and outcome analysis. The outputs of which are shared directly or indirectly with the NHS to support improvements in patient care. All outputs are aggregated with small numbers suppressed in line with the HES analysis guide. In addition to this analytical work, Harvey Walsh also provide a tool called AXON 360. AXON 360 is an online tool that is hosted within the Harvey Walsh offices in a secure server room from a dedicated network. AXON 360 incorporates aggregated HES, QOF and primary care prescribing data, this tool allows users to analyse healthcare data, derive a range of insights and produce reports to support improvements in patient care and provide insights and evidence for commissioners and healthcare professionals. Data is also analysed for the purposes of informing commissioners, clinicians and clinical networks on uptake on innovation, national policy guidelines, including NICE, patient pathways, benchmarking and disease burden analytics. The insights and analysis may be on a national level or down to individual practice level. All data is aggregated and small numbers are suppressed, including secondary suppression as per the HES analysis guide. The outputs that utilize the HES data are provided directly to the NHS and AHSNs or will be provided indirectly to the NHS via the pharmaceutical and device companies and AHSNs using bespoke outputs, reports, dashboards, research papers and via a tool called AXON 360 produced by Harvey Walsh. All of the outputs from the analysis of HES are used directly or indirectly for the provision or promotion of health and improved patient care and are not wholly commercial. Each purpose within this application (including the Axon tool) other than one specific project stated here (AF project) are only permitted to use 5 years of HES data. An AF project is already underway and is looking at temporal trends over the past 10 years. The project is a collaboration between Harvey Walsh, Greater Manchester ASHN and University Hospital Leeds; it will produce data urgently awaited by the NHS Commissioning Board. The provision of the 10 years of data for this specific project is critical to this analysis and subsequent publication which seeks to propose an optimum pathway for patients with AF at risk of a stroke	Harvey Walsh have received data on a monthly basis for the past 6 years. Harvey Walsh processes this data in the following ways: The raw flat HES files are downloaded from the SEFT server onto a drive on a secure dedicated in house server within Harvey Walsh offices in Runcorn. The data are then processed with costing algorithms applying tariffs to HRGs and all records are imported into two different databases within SQL Server - an archive database that has every requested column from the data files included, and a summary database that has a subset of columns for speed of processing. A verification process takes place on row count and then additional processing is undertaken applying the NHS cost algorithms and readmission calculations, the data is then ready to be made available for further use. The data are then split in two ways, for bespoke analysis by the SQL Analyst team and for the AXON 360 system. For bespoke analysis the refreshed data from the summary database is transferred via an internal secure network to 4 named analysts desktop SSD drives within SQL server. Only substantive employees of Harvey Walsh have access to the record level data in SQL and each of their machines is physically secure within a locked office and has 256 bit encryption and password protection. The employees who access record level HES data are logged on an access control register and are under contractual obligations with regards the safe and secure processing of sensitive data. Harvey Walsh is ISO27001/2013 compliant and all staff are fully trained on HES and undergo regular data security training. Prior to presentation to customers all outputs produced are checked by a Manager to ensure compliance to the HES Analysis user guide (i.e. aggregated data with small numbers suppressed) and all outputs have the stated purposes included and since 2015 have been recorded on a data output register. In addition a Privacy Impact Assessment (PIA) is now done on all HES analysis prior to commencement of a project. The AXON 360 system is processed as follows, the data are aggregated to a spell level (all related episodes made into a single spell row), and then this is further aggregated to an organisation and code level (ICD10, OPCS, HRG) when being rolled up into an OLAP cube. AXON 360 does not display individual episode or spell level data, the data are aggregated to an organisation or code level. The cube aggregates data at a diagnosis or organisation level will be accessed via the AXON application’s server code via a stored procedure on SQL Server. This allows another layer of security between the data and the AXON application layer. The data viewable in an AXON 360 report is pre-processed, aggregated and suppressed. AXON 360 is on a separate dedicated server on its own segregated network. This means that there is no access to the raw underlying HES data that is stored on a separate dedicated server within the Harvey Walsh facility. Harvey Walsh have also provided documented methodology to illustrate that the small number suppression they employ actually goes beyond the HES Analysis Guide providing further assurances. No record level data is provided to any third party organisation in any format. The only output will be aggregated data with small numbers suppressed as described and in line with the HES Analysis Guide. The data are not used to target sales individuals towards specific healthcare professionals and the data are not used within sales collateral used by sales/marketing teams this includes sales brochures, emails, direct mailing or advertising of pharmaceutical products. No record level data are transferred, stored or processed outside of the Harvey Walsh facility in Cheshire. The data already supplied and the data to be disseminated as described in this agreement will be used solely for the purposes outlined within this agreement. A data destruction certificate is required for data previously supplied that is not covered within this agreement.	Harvey Walsh undertake numerous projects utilizing HES on a yearly basis, approximately 100 distinct projects were completed in 2016/17. The outputs are varied and in different formats ranging from reports, charts, dashboards, budget impact models, health economic models, system dynamic models, health economic analysis, risk stratification, research papers and publications and AXON 360, see example at end of document. These outputs have achieved many useful things including changes in patient care, reduction in costs, uptake and monitoring of NHS policy. Harvey Walsh have a number of rolling contracts and Harvey Walsh expect to produce outputs on these up until at least 2019. Any output or analysis is ultimately delivered to improve patient care, pathways or healthcare services. Further details of outputs follow in the Benefits Section of this application. The retention of 10 year data will support the project looking at temporal trends in AF. The planned publication seeks to demonstrate how changes in the NHS have benefited this group of patients. Publications are planned for March 2018.	Harvey Walsh has held a data sharing agreement for over 8 years and has provided services to the NHS (NHS England, the NHS Board, AHSNs, trusts, CCGs, GP practices) and other healthcare related companies (Pharmaceutical and Device Companies, Patient Groups and Charities) during this time. Harvey Walsh have contracts which run through to 2019 and beyond and would expect to provide HES outputs through to this time. The solutions and projects that Harvey Walsh undertake have utilized cohort data from 2006 to date, and have been used for numerous different projects. The benefits that are provided from the analysis and insights of the HES data are direct and indirect to the patient and health and social care environment. Below are examples of direct benefits to patients and the NHS: 1: NHS England issued Clinical Commissioning Policy: Vagal Nerve Stimulation for Epilepsy April 2013 Reference: NHSCB/D04/P/d. Vagal Nerve Stimulation is used in refractory and drug resistant Epilepsy. Working with a device company and in collaboration with a Neurology Clinical Guidelines group analysis using HES has been undertaken to determine the: • Burden of Epilepsy and Variance across England demonstrating the patient pathways into specialist care • Modelled the capacity and potential requirements of surgical units so that patients who are suitable for treatment gain earlier access to treatment A health economic evaluation was undertaken on the healthcare utilisation pre and post implant to provide evidence to commissioners and NHS England on the benefits for patients as unplanned activity reduces as does cost to the NHS. This evaluation has been published Burke T, Hughes D, Forsey J, Bunker M, Bhattacharya D, Smithson WH, A Study of the Impact of VNS on Health Care Utilisation in England, SEIZURE: European Journal of Epilepsy (2015), http://dx.doi.org/10.1016/j.seizure.2015.11.002 2: Idiopathic Pulmonary Fibrosis Pathway Analysis: This project was undertaken with a commercial client for use in collaborative working with the NHS. Idiopathic pulmonary fibrosis (IPF) is a chronic and ultimately fatal disease characterized by a progressive decline in lung function. The term pulmonary fibrosis means scarring of lung tissue and is the cause of worsening dyspnoea (shortness of breath). Fibrosis is usually associated with a poor prognosis. In its earlier stages IPF mimics many routine chest conditions and requires specialist diagnostics to make a definitive diagnosis. Once diagnosed it is managed under specialist commissioning arrangements at key specialist centres such as the Royal Brompton. It’s quite possible for a patient to have many hospital admissions to local Hospital Trusts then specialist Chest Units before receiving a final diagnosis and referral to the appropriate Tertiary Specialist Centres. This makes determining a patient pathway difficult and the planning and commissioning of IPF services complicated. Tertiary referral trusts and their clinicians face challenges in planning and optimising capacity of IPF services due to the paucity of service data for this rare condition and the wide geographical distribution of patients. The IPF Pathway Analysis and Dashboard output supports the NHS to identify the current resource management of patients who may be identified as Idiopathic Pulmonary Fibrosis (IPF) patients in England. The dashboard provides a comprehensive Health Episode Statistic (HES) patient pathway and outcomes analysis from 2009 – 2014 across different co-morbidities and organisations. The data is displayed as reports and bespoke dashboards in order to support the NHS to plan for future patient and resource management requirements, for patients who may have IPF. In addition, by determining IPF records in 2014 to go back through the HES record for those patients to the first admission for a chest related condition and calculate two factors: • The Time from first Chest Condition Admission to diagnosis of IPF • To determine any patterns of Chest Conditions admissions that could be used as a marker for patients at risk of developing IPF to speed referral to the appropriate Tertiary Referral Specialist Trust for definitive diagnostics with aim of diagnosing and treating IPF earlier. The numbers of patients in England has been static around 5250 for the last 5 years accounting for approximately 7000 spells per year and costing on average £15m per year. For the first time it has been possible to say that the average time from first presentation with a chest condition to diagnosis with IPF is between 400 and 600 days. The top 5 chest conditions that account for the majority of hospital admissions prior to a diagnosis with IPF are: 1. Lobar Pneumonia Unspecified 2. Chronic Obstructive pulmonary disease with acute lower respiratory infection 3. Abnormal findings on diagnostic imaging of lung 4. Pneumonia unspecified 5. Interstitial Pulmonary disease unspecified Which presents an opportunity for the NHS to work to identify the types of patients with this pattern of admissions for definitive IPF diagnostics so reducing the time from presentation to diagnosis and treatment. Therefore directly improving outcomes for patients and reducing costs for the NHS. 3: Analysis of TURPS Patient Pathways for England: This work was undertaken with a commercial client to support NICE submission and to provide data to NHS England on innovative surgical techniques. Transurethral resection of the prostate (TURP) is a type of prostate surgery done to relieve moderate to severe urinary symptoms caused by an enlarged prostate, a condition known as benign prostatic hyperplasia (BPH). During TURP, a combined visual and surgical instrument (resectoscope) is inserted through the tip of the penis and into the (urethra). The urethra is surrounded by the prostate. Using the resectoscope, excess prostate tissue is removed that's blocking urine flow and increases the size urethra that allows the patient to empty their bladder. Current surgical approaches like TURP can leave permanent side effects such as urinary incontinence and erectile dysfunction. The aim of this project was to understand how many Patients had BPH, the number of TURPs undertaken for BPH and the complications associated with those TURPs and the long term impact of those complications on patient hospital admissions. This would indicate the real costs and patient impact of TURP as a baseline against which alternative less invasive procedures might be evaluated. A longitudinal analysis was performed on the HES Data Set for England and all records with a recorded diagnosis of BPH in 2013 who had within the same year undergone TURP. Harvey Walsh then identified subsequent admissions over the following year for the complications associated with TURP for those patients with a diagnosis of BPH also having a TURP in 2013. This data was presented nationally, and by CCG and hospital trust. Summary Post-operative complications add approximately 23% to the actual costs of performing TURP procedures and have a significant impact on patients’ lives. It is clear that clinically patients who undergo TURP and subsequently are admitted for a TURP complication will have that recorded in their notes. However, as the admissions for complication do not generally coincide with the spell during which the procedure is performed it may be very difficult for commissioners to see the impact in terms on subsequent hospitals admissions LOS and costs for patient undergoing TURP without investing scarce time and resource for data analysis for a procedure that is routine for this condition. Further as expected the peak in complication numbers & costs occurs in the year following procedure. However, in the 5 years following surgery many of the complications persist in reoccurring spells. TURP remains the standard procedure to relieve moderate to severe urinary symptoms caused by an enlarged prostate. In reviewing alternative procedures to TURP those that are less invasive but preclude the complications from TURP could have an impact on service cost and efficiency but potentially patient outcome not just in year but in the longer term. It was on the basis of this data analysis that the client was able to secure a meeting with the Office of Life Sciences to discuss complications of TURP as part of their remit to look for opportunities for innovation in patient care. This has now spread to NICE and there are on-going meetings to discuss the management pathways for these patients providing a direct benefit to patients by offering innovative procedures with less chance of complications whilst saving money for the NHS. 4. Renal Cancer Commissioning Insights Dashboard. (Commissioned by Pfizer) A quarterly refreshed insights tool that provides aggregated metrics to Providers and CCGs on: • Numbers of patients/growth/decline • Time to referral • Pathway of treatment • Healthcare utilisation • Variation and comparison The Pathways for Renal Cancer vary across the country. The objective of this project was to provide information and insights on current referral and management pathways for Renal Cancer to Commissioners and Clinicians. So that gaps in service provision and variation in care can be addressed by the development of local education and pathway support. This programme brings benefits to patients as it addresses service variation and supports improvement in quicker referral times to specialist centres for treatment and interventions 5. IBS Insights Tool (Commissioned by Allergan) A monthly refreshed dashboard that provides aggregated metrics to CCGs and Clinicians on the IBS Pathways showing: • Rates of scopes • Variation • Burden of IBS • Capacity mapping Irritable Bowel Syndrome (IBS) is often misdiagnosed and patients have unnecessary scopes which result in high costs for CCGs and capacity issues for Providers. The objective of this project was to show the current referral and management pathways for IBS to CCGs and provide the data and insights for service reviews and the development of local protocols to support appropriate referrals and reduce the numbers of referrals and thus reduce costs. This programme provides benefits to patients by reducing physical diagnostic scopes which are often not required and benefits to providers as capacity can be reduced and CCGs save money on scopes. 6. Atrial Fibrillation Insights Tool (Joint working Greater Manchester AHSN and Diatchi Sankyo) A monthly refreshed model with aggregated data (HES, QOF, Prescribing and GRASP) which maps the local impact and variation of AF across Greater Manchester Atrial Fibrillation is a ACS and QOF condition which is actively managed by GPs and CCGs. However significant numbers of patients still enter hospital as unplanned emergency admissions each month. The aim of this programme was to provide an in-depth analysis of AF across Greater Manchester, highlighting variance in care, uptake of NICE Guidance. Areas where performance and outcomes where not as good as expected were supported by educational and transformational programmes via the AHSN. This programme provides benefits to patients by improving how they are treated and aims to reduce admissions to hospital. This in turn benefits CCGs and Providers by reducing costs and unplanned admissions. 7. Saving Sight Campaign (commissioned by RNIB) Analysis of variation of Cataract Procedures across England by CCG to determine time to treatment. Originally commissioned in 2013, Harvey Walsh undertook a deep dive analysis on Cataract services in England to develop a publication Surgery Deferred Sight Denied Report 2013. This report showed significant variation in access to first and second eye surgery. This resulted in a number of interventions and educational support by the RNIB. In 2016 Harvey Walsh were asked by the RNIB to undertake a refreshed analysis based on the 2013 study. This analysis shows increases in treatment and a decrease in the variation of care with over 100,000 additional procedures taking place since the initial study. This programme has produced improvements in time to treatment and the treatment of second eye blindness resulting in benefits for patients and the wider healthcare arena. 8. Impact of Vaccination on the rates of Pneumococcal Disease in England since 2006 (Commissioned by Pfizer) Analysis of HES data to produce aggregated national analysis on the rates of Pneumococcal Disease over 10 years The complications of Pneumonia can lead to devastating outcomes such as death, meningitis and sepsis. The objective of this analysis was to determine whether vaccines have had an impact on these complications and the outputs will be used to provide evidence to NICE and other NHS bodies to support their decisions on future vaccine programmes. This work has provided benefits to patients by providing outputs to support further vaccine programmes which will result in less people developing the consequence of Pneumonia Infections.
HEALTH AND SAFETY EXECUTIVE	HEALTH AND SAFETY EXECUTIVE	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The overall aims of the study are to monitor the health of workers in Great Britain who use pesticides as a part of their job, and to gain a better understanding of the relationship between long-term exposure to pesticides and health. This will help the Health & Safety Laboratory (HSL), which is part of Health & Safety Executive, to make informed decisions on future policies. Monitoring mortality and cancer incidence is an important part of this, and will provide information on the health risk of individuals working with pesticides. In addition, the availability of routinely collected data for research purposes is increasing, and the study would like to ensure that it can make full use of these if required in the future. Some datasets may be region specific, and so it is important that HSL can track study participants even after HSL have lost contact with them. Therefore HSL are also requesting information on emigrations and the health authority cipher. The specific objectives of the processing will be to: 1) ensure that HSL do not attempt to contact study members who have died or have emigrated outside of Great Britain; 2) compare cancer incidence and mortality among study members with the general population, and between different groups of study members who have worked with pesticides in different ways, for example comparing people who have used different pesticides; and 3) keep track of study members' area of residence / registration to aid future potential data linkages. For information only, a long-term goal is to enable other researchers to use the data generated by this study. Specific consent has been sought from study members to do this. Any additional uses of the data would be subject to the approval of future applications to NHS Digital. The previous data request for this study was for NHS Digital (formerly known as Health and Social Care Information Centre) to notify HSL of which study members had died. This prevented HSL contacting deceased individuals in the January 2016 mailing and potentially causing relatives distress. This was a one-off request, and HSL are now requesting on-going patient tracking. HSE do not current hold any HES data for the purpose of this study	The data will be processed at the Health & Safety Laboratory (HSL), which is part of the Health & Safety Executive (HSE). All data transfers between NHS Digital and HSL will be undertaken using the NHS Digital's secure file transfer system. Any information received from NHS Digital will be downloaded directly onto a restricted access network drive, dedicated to the study. Access to the data is limited to authorised substantive employees of the HSE exclusively within the HSL at the Buxton address. No other HSE employees will access the data at any other location. No data will be accessible to third parties outside of HSE. The data will only be used for the purpose as stated. The flow of data will be as follows: 1) The HSL study team will send NHS Digital the following information on study members where available: study ID; NHS number; Forename; Middle name; Surname; Date of birth; Sex and Address (including postcode. This will enable linkage to be undertaken by NHS Digital. 2) NHS Digital will provide the HSL study team the requested records. 3a) The study database is currently under development. Until this is finished, the information provided by NHS Digital will be stored electronically on the restricted access network drive. HSL expect that the study database will be ready for data entry by April 2017 at the earliest. When finished, the database will reside in HSL on an SQL server at Buxton, which HSL control. The restricted access network drive is on an HSL server at Harpur Hill, Buxton, with access only granted to authorised members of staff. The requested data are needed before the study database is complete for two reasons. First, the data required for objective 1 (alive/dead status) is needed for any mail outs before the database will be complete. Second, it will greatly help with database development to have access to the raw data from NHS Digital. 3b) Once complete, all information received from NHS Digital will be uploaded onto the study database. Identifiable information that HSL holds will either be stored on the same database as the research data but partitioned/secured using SQL schema or a bespoke service API, or will be held on a separate database. Restrictions will be in place so that only study team members with the correct permissions will be able to access data received from the NHS Digital. In addition, the database will be encrypted. 4) Data required for the different objectives will be extracted from the database by study team members with the correct permissions. All study team members are substantive employees of the data controller. Only the information required will be extracted. Extracted datasets will contain the minimum identifiable fields required, and will be saved on the restricted access network drive for the study. All data processing will be conducted on the restricted access network drive. The information from NHS Digital will be linked to research data collected from the participants throughout the duration of the study to enable analysis. This includes the following information: - responses to a baseline questionnaire collecting information on work history, previous work with pesticides, general health, family medical history, lifestyle, diet, tobacco and alcohol use, and socio-economic circumstances; - responses to a questionnaire on current pesticide use; - future updates to this information.	The expected outputs from processing are threefold: 1) The study participants are contacted annually in order to send them the study Newsletter and follow-up questionnaires. It is therefore important to maintain an up-to-date mailing list to ensure that the study team does not attempt to contact any deceased participants. Receiving information on death registrations and the Members and Postings lists will enable the study team to keep the mailing list up-to-date into the future. 2) The main outcomes of interest to the study are cancer incidence and mortality. The statistical analyses will use these outcomes to compare the health of the study members with the general population, and compare groups of study members with different exposures. It is critical for analytical purposes to know which participants have died or emigrated so that their end of follow-up dates can be incorporated in any prospective data analysis. The resulting outputs from this research study will be published. How the outputs will be published will be determined by the study team to ensure that the findings reach the most appropriate audience. The resulting outputs from the analysis of the study data may be published in Health & Safety Executive Research Reports (http://www.hse.gov.uk/research/rrhtm/), which will be made freely available to researchers and the public on the Health & Safety Executive website. Peer-reviewed journal publications will be prepared where appropriate, and these will be 'open access' so that both researchers and the public can view papers. The most appropriate journals will be decided upon at the time of publication. Examples of journals other similar studies have published in previously include 'Occupational Medicine', 'British Journal of Cancer', and 'Annals of Occupational Hygiene'. Results may also be presented at conferences to researchers where appropriate. As with journals, the most appropriate conference will be decided upon at the time. Examples of conferences this study and other similar studies have presented at previously include The International Epidemiology in Occupational Health (EPICOH) Conference, The UK & Ireland Occupational & Environmental Epidemiology Conference, and The British Occupational Hygiene Society Annual Conference. As a publicly funded research study, it is HSE policy to ensure that outputs are published and that the publications are ‘open access’ so that they are accessible to all. However it is not possible to determine exactly where each set of findings will be published. At this point HSE are providing a list of possibilities based on HSE's experience with similar studies. Outputs may be published in more than one format – for example as a research report and as a conference presentation. A newsletter is produced annually to keep study members up-to-date with progress on the study. This will also include study results and details of where to find more information. The results will take the form of aggregated data, and will typically include summary statistics, and standardised mortality/incidence/admission ratios or relative risks. Individuals will not be identifiable in these results and if there are small numbers involved in any aggregated data, then these will be suppressed in accordance with the HES Analysis Guide. This is a long-term cohort study which will entail the initial analysis of the baseline data, followed by periodic analysis of the health outcome data (self-reported ill-health, cancers, deaths and HES data) collected during follow-up. The baseline data analysis is on-going; the first publications (two Health & Safety Executive Research Reports and one journal publication) were published during 2016. The first peer reviewed paper describing the establishment of the cohort is expected to be published in 2017. Further baseline data analyses (for example of specific groups of self-reported ill health, such as respiratory health) will be undertaken until sufficient follow-up data is available for prospective analysis. The timetable for the periodic analyses will be determined by the Health & Safety Executive to meet its requirements. The data will not be used for commercial purposes. Under the previous data request to NHS Digital in 2015, participants who had consented and were found to be alive (as identified by NHS Digital) were sent a newsletter and questionnaire to complete. These questionnaires will feed in to the study outputs described above. 3) Maintaining an up-to-date list of study participants will help the study team keep track of study members and will aid future potential data linkages. This will help to reduce losses-to-follow up and attrition in participant numbers as the study matures.	The PIPAH study is a detailed study of individuals who use pesticides as part of their work and are potentially exposed to low levels of pesticides over a long period. It was established by the Health & Safety Executive in order to monitor the long-term health of these individuals because other systems of surveillance for pesticide exposures only cover acute health outcomes. The study helps the Health & Safety Executive to determine the long-term safety of pesticides licensed for use in GB and is an integral part of the Health & Safety Executive's commitment to protecting the health of people at work. The study is a valuable resource that will make a worthwhile contribution to the wider literature on pesticides and health, and help to elucidate some of the current inconsistencies in the literature. The study is part of a consortium of agricultural cohort studies (AGRICOH) and in future will partake in pooling studies to investigate specific health outcomes, including rare health outcomes which cannot be addressed by individual studies (permission to pool data will be requested from NHS Digital before any data are shared). This will provide further benefits to the knowledge base on pesticides beyond those which any individual study can provide. Dissemination of findings by various means will help to inform pesticide users, researchers, regulatory agencies and the wider public about the safety of pesticides licensed for use in GB. As a long-term study, the value of the findings will increase as the length of follow-up increases. Currently there are no set target dates for any outputs beyond periodic analysis of health outcomes to monitor the health of the participants. Further analyses to address specific knowledge gaps will be determined by the Health & Safety Executive. The results of this study, and the greater understanding of any potential risks involved in pesticide use which the study will provide, will help to inform the Health & Safety Executive’s future policy with respect to licensing particular pesticides. This Health & Safety Executive funded study is operationally carried by out at HSE Buxton. The outputs from this study will contribute to the body of evidence about the safety of licensed pesticides which the Health & Safety Executive will take into account in any decision process.
HEALTH AND SAFETY EXECUTIVE	HEALTH AND SAFETY EXECUTIVE	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The overall aims of the study are to monitor the health of workers in Great Britain who use pesticides as a part of their job, and to gain a better understanding of the relationship between long-term exposure to pesticides and health. This will help the Health & Safety Laboratory (HSL), which is part of Health & Safety Executive, to make informed decisions on future policies. Monitoring mortality and cancer incidence is an important part of this, and will provide information on the health risk of individuals working with pesticides. In addition, the availability of routinely collected data for research purposes is increasing, and the study would like to ensure that it can make full use of these if required in the future. Some datasets may be region specific, and so it is important that HSL can track study participants even after HSL have lost contact with them. Therefore HSL are also requesting information on emigrations and the health authority cipher. The specific objectives of the processing will be to: 1) ensure that HSL do not attempt to contact study members who have died or have emigrated outside of Great Britain; 2) compare cancer incidence and mortality among study members with the general population, and between different groups of study members who have worked with pesticides in different ways, for example comparing people who have used different pesticides; and 3) keep track of study members' area of residence / registration to aid future potential data linkages. For information only, a long-term goal is to enable other researchers to use the data generated by this study. Specific consent has been sought from study members to do this. Any additional uses of the data would be subject to the approval of future applications to NHS Digital. The previous data request for this study was for NHS Digital (formerly known as Health and Social Care Information Centre) to notify HSL of which study members had died. This prevented HSL contacting deceased individuals in the January 2016 mailing and potentially causing relatives distress. This was a one-off request, and HSL are now requesting on-going patient tracking. HSE do not current hold any HES data for the purpose of this study	The data will be processed at the Health & Safety Laboratory (HSL), which is part of the Health & Safety Executive (HSE). All data transfers between NHS Digital and HSL will be undertaken using the NHS Digital's secure file transfer system. Any information received from NHS Digital will be downloaded directly onto a restricted access network drive, dedicated to the study. Access to the data is limited to authorised substantive employees of the HSE exclusively within the HSL at the Buxton address. No other HSE employees will access the data at any other location. No data will be accessible to third parties outside of HSE. The data will only be used for the purpose as stated. The flow of data will be as follows: 1) The HSL study team will send NHS Digital the following information on study members where available: study ID; NHS number; Forename; Middle name; Surname; Date of birth; Sex and Address (including postcode. This will enable linkage to be undertaken by NHS Digital. 2) NHS Digital will provide the HSL study team the requested records. 3a) The study database is currently under development. Until this is finished, the information provided by NHS Digital will be stored electronically on the restricted access network drive. HSL expect that the study database will be ready for data entry by April 2017 at the earliest. When finished, the database will reside in HSL on an SQL server at Buxton, which HSL control. The restricted access network drive is on an HSL server at Harpur Hill, Buxton, with access only granted to authorised members of staff. The requested data are needed before the study database is complete for two reasons. First, the data required for objective 1 (alive/dead status) is needed for any mail outs before the database will be complete. Second, it will greatly help with database development to have access to the raw data from NHS Digital. 3b) Once complete, all information received from NHS Digital will be uploaded onto the study database. Identifiable information that HSL holds will either be stored on the same database as the research data but partitioned/secured using SQL schema or a bespoke service API, or will be held on a separate database. Restrictions will be in place so that only study team members with the correct permissions will be able to access data received from the NHS Digital. In addition, the database will be encrypted. 4) Data required for the different objectives will be extracted from the database by study team members with the correct permissions. All study team members are substantive employees of the data controller. Only the information required will be extracted. Extracted datasets will contain the minimum identifiable fields required, and will be saved on the restricted access network drive for the study. All data processing will be conducted on the restricted access network drive. The information from NHS Digital will be linked to research data collected from the participants throughout the duration of the study to enable analysis. This includes the following information: - responses to a baseline questionnaire collecting information on work history, previous work with pesticides, general health, family medical history, lifestyle, diet, tobacco and alcohol use, and socio-economic circumstances; - responses to a questionnaire on current pesticide use; - future updates to this information.	The expected outputs from processing are threefold: 1) The study participants are contacted annually in order to send them the study Newsletter and follow-up questionnaires. It is therefore important to maintain an up-to-date mailing list to ensure that the study team does not attempt to contact any deceased participants. Receiving information on death registrations and the Members and Postings lists will enable the study team to keep the mailing list up-to-date into the future. 2) The main outcomes of interest to the study are cancer incidence and mortality. The statistical analyses will use these outcomes to compare the health of the study members with the general population, and compare groups of study members with different exposures. It is critical for analytical purposes to know which participants have died or emigrated so that their end of follow-up dates can be incorporated in any prospective data analysis. The resulting outputs from this research study will be published. How the outputs will be published will be determined by the study team to ensure that the findings reach the most appropriate audience. The resulting outputs from the analysis of the study data may be published in Health & Safety Executive Research Reports (http://www.hse.gov.uk/research/rrhtm/), which will be made freely available to researchers and the public on the Health & Safety Executive website. Peer-reviewed journal publications will be prepared where appropriate, and these will be 'open access' so that both researchers and the public can view papers. The most appropriate journals will be decided upon at the time of publication. Examples of journals other similar studies have published in previously include 'Occupational Medicine', 'British Journal of Cancer', and 'Annals of Occupational Hygiene'. Results may also be presented at conferences to researchers where appropriate. As with journals, the most appropriate conference will be decided upon at the time. Examples of conferences this study and other similar studies have presented at previously include The International Epidemiology in Occupational Health (EPICOH) Conference, The UK & Ireland Occupational & Environmental Epidemiology Conference, and The British Occupational Hygiene Society Annual Conference. As a publicly funded research study, it is HSE policy to ensure that outputs are published and that the publications are ‘open access’ so that they are accessible to all. However it is not possible to determine exactly where each set of findings will be published. At this point HSE are providing a list of possibilities based on HSE's experience with similar studies. Outputs may be published in more than one format – for example as a research report and as a conference presentation. A newsletter is produced annually to keep study members up-to-date with progress on the study. This will also include study results and details of where to find more information. The results will take the form of aggregated data, and will typically include summary statistics, and standardised mortality/incidence/admission ratios or relative risks. Individuals will not be identifiable in these results and if there are small numbers involved in any aggregated data, then these will be suppressed in accordance with the HES Analysis Guide. This is a long-term cohort study which will entail the initial analysis of the baseline data, followed by periodic analysis of the health outcome data (self-reported ill-health, cancers, deaths and HES data) collected during follow-up. The baseline data analysis is on-going; the first publications (two Health & Safety Executive Research Reports and one journal publication) were published during 2016. The first peer reviewed paper describing the establishment of the cohort is expected to be published in 2017. Further baseline data analyses (for example of specific groups of self-reported ill health, such as respiratory health) will be undertaken until sufficient follow-up data is available for prospective analysis. The timetable for the periodic analyses will be determined by the Health & Safety Executive to meet its requirements. The data will not be used for commercial purposes. Under the previous data request to NHS Digital in 2015, participants who had consented and were found to be alive (as identified by NHS Digital) were sent a newsletter and questionnaire to complete. These questionnaires will feed in to the study outputs described above. 3) Maintaining an up-to-date list of study participants will help the study team keep track of study members and will aid future potential data linkages. This will help to reduce losses-to-follow up and attrition in participant numbers as the study matures.	The PIPAH study is a detailed study of individuals who use pesticides as part of their work and are potentially exposed to low levels of pesticides over a long period. It was established by the Health & Safety Executive in order to monitor the long-term health of these individuals because other systems of surveillance for pesticide exposures only cover acute health outcomes. The study helps the Health & Safety Executive to determine the long-term safety of pesticides licensed for use in GB and is an integral part of the Health & Safety Executive's commitment to protecting the health of people at work. The study is a valuable resource that will make a worthwhile contribution to the wider literature on pesticides and health, and help to elucidate some of the current inconsistencies in the literature. The study is part of a consortium of agricultural cohort studies (AGRICOH) and in future will partake in pooling studies to investigate specific health outcomes, including rare health outcomes which cannot be addressed by individual studies (permission to pool data will be requested from NHS Digital before any data are shared). This will provide further benefits to the knowledge base on pesticides beyond those which any individual study can provide. Dissemination of findings by various means will help to inform pesticide users, researchers, regulatory agencies and the wider public about the safety of pesticides licensed for use in GB. As a long-term study, the value of the findings will increase as the length of follow-up increases. Currently there are no set target dates for any outputs beyond periodic analysis of health outcomes to monitor the health of the participants. Further analyses to address specific knowledge gaps will be determined by the Health & Safety Executive. The results of this study, and the greater understanding of any potential risks involved in pesticide use which the study will provide, will help to inform the Health & Safety Executive’s future policy with respect to licensing particular pesticides. This Health & Safety Executive funded study is operationally carried by out at HSE Buxton. The outputs from this study will contribute to the body of evidence about the safety of licensed pesticides which the Health & Safety Executive will take into account in any decision process.
HEALTH AND SAFETY EXECUTIVE	HEALTH AND SAFETY EXECUTIVE	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The overall aims of the study are to monitor the health of workers in Great Britain who use pesticides as a part of their job, and to gain a better understanding of the relationship between long-term exposure to pesticides and health. This will help the Health & Safety Laboratory (HSL), which is part of Health & Safety Executive, to make informed decisions on future policies. Monitoring mortality and cancer incidence is an important part of this, and will provide information on the health risk of individuals working with pesticides. In addition, the availability of routinely collected data for research purposes is increasing, and the study would like to ensure that it can make full use of these if required in the future. Some datasets may be region specific, and so it is important that HSL can track study participants even after HSL have lost contact with them. Therefore HSL are also requesting information on emigrations and the health authority cipher. The specific objectives of the processing will be to: 1) ensure that HSL do not attempt to contact study members who have died or have emigrated outside of Great Britain; 2) compare cancer incidence and mortality among study members with the general population, and between different groups of study members who have worked with pesticides in different ways, for example comparing people who have used different pesticides; and 3) keep track of study members' area of residence / registration to aid future potential data linkages. For information only, a long-term goal is to enable other researchers to use the data generated by this study. Specific consent has been sought from study members to do this. Any additional uses of the data would be subject to the approval of future applications to NHS Digital. The previous data request for this study was for NHS Digital (formerly known as Health and Social Care Information Centre) to notify HSL of which study members had died. This prevented HSL contacting deceased individuals in the January 2016 mailing and potentially causing relatives distress. This was a one-off request, and HSL are now requesting on-going patient tracking. HSE do not current hold any HES data for the purpose of this study	The data will be processed at the Health & Safety Laboratory (HSL), which is part of the Health & Safety Executive (HSE). All data transfers between NHS Digital and HSL will be undertaken using the NHS Digital's secure file transfer system. Any information received from NHS Digital will be downloaded directly onto a restricted access network drive, dedicated to the study. Access to the data is limited to authorised substantive employees of the HSE exclusively within the HSL at the Buxton address. No other HSE employees will access the data at any other location. No data will be accessible to third parties outside of HSE. The data will only be used for the purpose as stated. The flow of data will be as follows: 1) The HSL study team will send NHS Digital the following information on study members where available: study ID; NHS number; Forename; Middle name; Surname; Date of birth; Sex and Address (including postcode. This will enable linkage to be undertaken by NHS Digital. 2) NHS Digital will provide the HSL study team the requested records. 3a) The study database is currently under development. Until this is finished, the information provided by NHS Digital will be stored electronically on the restricted access network drive. HSL expect that the study database will be ready for data entry by April 2017 at the earliest. When finished, the database will reside in HSL on an SQL server at Buxton, which HSL control. The restricted access network drive is on an HSL server at Harpur Hill, Buxton, with access only granted to authorised members of staff. The requested data are needed before the study database is complete for two reasons. First, the data required for objective 1 (alive/dead status) is needed for any mail outs before the database will be complete. Second, it will greatly help with database development to have access to the raw data from NHS Digital. 3b) Once complete, all information received from NHS Digital will be uploaded onto the study database. Identifiable information that HSL holds will either be stored on the same database as the research data but partitioned/secured using SQL schema or a bespoke service API, or will be held on a separate database. Restrictions will be in place so that only study team members with the correct permissions will be able to access data received from the NHS Digital. In addition, the database will be encrypted. 4) Data required for the different objectives will be extracted from the database by study team members with the correct permissions. All study team members are substantive employees of the data controller. Only the information required will be extracted. Extracted datasets will contain the minimum identifiable fields required, and will be saved on the restricted access network drive for the study. All data processing will be conducted on the restricted access network drive. The information from NHS Digital will be linked to research data collected from the participants throughout the duration of the study to enable analysis. This includes the following information: - responses to a baseline questionnaire collecting information on work history, previous work with pesticides, general health, family medical history, lifestyle, diet, tobacco and alcohol use, and socio-economic circumstances; - responses to a questionnaire on current pesticide use; - future updates to this information.	The expected outputs from processing are threefold: 1) The study participants are contacted annually in order to send them the study Newsletter and follow-up questionnaires. It is therefore important to maintain an up-to-date mailing list to ensure that the study team does not attempt to contact any deceased participants. Receiving information on death registrations and the Members and Postings lists will enable the study team to keep the mailing list up-to-date into the future. 2) The main outcomes of interest to the study are cancer incidence and mortality. The statistical analyses will use these outcomes to compare the health of the study members with the general population, and compare groups of study members with different exposures. It is critical for analytical purposes to know which participants have died or emigrated so that their end of follow-up dates can be incorporated in any prospective data analysis. The resulting outputs from this research study will be published. How the outputs will be published will be determined by the study team to ensure that the findings reach the most appropriate audience. The resulting outputs from the analysis of the study data may be published in Health & Safety Executive Research Reports (http://www.hse.gov.uk/research/rrhtm/), which will be made freely available to researchers and the public on the Health & Safety Executive website. Peer-reviewed journal publications will be prepared where appropriate, and these will be 'open access' so that both researchers and the public can view papers. The most appropriate journals will be decided upon at the time of publication. Examples of journals other similar studies have published in previously include 'Occupational Medicine', 'British Journal of Cancer', and 'Annals of Occupational Hygiene'. Results may also be presented at conferences to researchers where appropriate. As with journals, the most appropriate conference will be decided upon at the time. Examples of conferences this study and other similar studies have presented at previously include The International Epidemiology in Occupational Health (EPICOH) Conference, The UK & Ireland Occupational & Environmental Epidemiology Conference, and The British Occupational Hygiene Society Annual Conference. As a publicly funded research study, it is HSE policy to ensure that outputs are published and that the publications are ‘open access’ so that they are accessible to all. However it is not possible to determine exactly where each set of findings will be published. At this point HSE are providing a list of possibilities based on HSE's experience with similar studies. Outputs may be published in more than one format – for example as a research report and as a conference presentation. A newsletter is produced annually to keep study members up-to-date with progress on the study. This will also include study results and details of where to find more information. The results will take the form of aggregated data, and will typically include summary statistics, and standardised mortality/incidence/admission ratios or relative risks. Individuals will not be identifiable in these results and if there are small numbers involved in any aggregated data, then these will be suppressed in accordance with the HES Analysis Guide. This is a long-term cohort study which will entail the initial analysis of the baseline data, followed by periodic analysis of the health outcome data (self-reported ill-health, cancers, deaths and HES data) collected during follow-up. The baseline data analysis is on-going; the first publications (two Health & Safety Executive Research Reports and one journal publication) were published during 2016. The first peer reviewed paper describing the establishment of the cohort is expected to be published in 2017. Further baseline data analyses (for example of specific groups of self-reported ill health, such as respiratory health) will be undertaken until sufficient follow-up data is available for prospective analysis. The timetable for the periodic analyses will be determined by the Health & Safety Executive to meet its requirements. The data will not be used for commercial purposes. Under the previous data request to NHS Digital in 2015, participants who had consented and were found to be alive (as identified by NHS Digital) were sent a newsletter and questionnaire to complete. These questionnaires will feed in to the study outputs described above. 3) Maintaining an up-to-date list of study participants will help the study team keep track of study members and will aid future potential data linkages. This will help to reduce losses-to-follow up and attrition in participant numbers as the study matures.	The PIPAH study is a detailed study of individuals who use pesticides as part of their work and are potentially exposed to low levels of pesticides over a long period. It was established by the Health & Safety Executive in order to monitor the long-term health of these individuals because other systems of surveillance for pesticide exposures only cover acute health outcomes. The study helps the Health & Safety Executive to determine the long-term safety of pesticides licensed for use in GB and is an integral part of the Health & Safety Executive's commitment to protecting the health of people at work. The study is a valuable resource that will make a worthwhile contribution to the wider literature on pesticides and health, and help to elucidate some of the current inconsistencies in the literature. The study is part of a consortium of agricultural cohort studies (AGRICOH) and in future will partake in pooling studies to investigate specific health outcomes, including rare health outcomes which cannot be addressed by individual studies (permission to pool data will be requested from NHS Digital before any data are shared). This will provide further benefits to the knowledge base on pesticides beyond those which any individual study can provide. Dissemination of findings by various means will help to inform pesticide users, researchers, regulatory agencies and the wider public about the safety of pesticides licensed for use in GB. As a long-term study, the value of the findings will increase as the length of follow-up increases. Currently there are no set target dates for any outputs beyond periodic analysis of health outcomes to monitor the health of the participants. Further analyses to address specific knowledge gaps will be determined by the Health & Safety Executive. The results of this study, and the greater understanding of any potential risks involved in pesticide use which the study will provide, will help to inform the Health & Safety Executive’s future policy with respect to licensing particular pesticides. This Health & Safety Executive funded study is operationally carried by out at HSE Buxton. The outputs from this study will contribute to the body of evidence about the safety of licensed pesticides which the Health & Safety Executive will take into account in any decision process.
HEALTH IQ LTD	HEALTH IQ LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Health iQ is a provider of data-based insight, who produce tools and reports used by health and social care to achieve the following broad aims: 1. Understand and quantify the burden of disease. 2. Support service improvement in terms of treatment and efficiency of service. Health iQ wishes to provide such insight to Healthcare Providers and the Life Sciences industry. Health iQ will use the data solely for the following purposes: 1. Vantage System and Related Support Vantage is an online system that produces aggregated, small-number suppressed, non-sensitive, non-identifiable HES-based dashboards and reports to support the delivery of healthcare. It supports the delivery of a range of key healthcare strategic priorities, including delivering the Five-Year Forward-View, Quality, Innovation, Productivity and Prevention (QIPP) targets and Joint Strategic Needs Assessment (JSNA) targets. Vantage enables users to: • Plan healthcare provision with the support of real world data. • Benchmark performance against peer groups. • Pinpoint areas of inefficiency. • Validate the impact of a service improvement programme or new pathway model. The users of Vantage are limited to the following: 1. NHS users (Provider Trusts, GPs, Commissioners including new NHS commissioning organisations/collaborations such as Vanguards and STPs, Area Teams, Strategic Clinical Networks (SCNs)). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, Department of Health (DH), NICE, Academic Health Science Networks (AHSNs)). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of Vantage can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced through sub-license agreement between Health iQ and users. Life Science organisations are a user of Vantage exclusively for the purpose of providing benefit to healthcare. As with all user groups, they will only ever have access to aggregated outputs and are bound by sub-license agreements which ensure the usage of the data is in line with this document. In addition, Health iQ insist that all users of the tool undergo information governance training by a Health iQ trainer, and all reports produced by the tool come with a pre-written disclaimer statement. 2. Reports Health IQ will produce reports either as responses to specific data requests, or as part of wider projects. These reports will take the form of suppressed, aggregated, non-sensitive and non-identifiable data tables. As these reports will be constructed in response to a specific need, the content will vary, though all conform to all the restrictions outlined in this document. Examples of such reports could be: • A report by Hospital on total activity which falls within a Best-Practice Tariff (BPT) area, and the proportion of such activity which achieved the BPT. • A report of the tariff cost of Irritable Bowel Syndrome (IBS) patients by CCG, including all related symptoms and associated conditions to produce a ‘true burden’ analysis of the cost of IBD (Irritable Bowel Disease) to the healthcare system. To be absolutely clear, reports will never: • Relate or link HES data to the use of commercially available products, such as the prescribing of an individual pharmaceutical product. • Present data in a way which patient or clinician identity can be identified, even by linking to other datasets. • Break suppression rules. The potential users of reports are: 1. NHS users (Provider Trusts, GPs, Commissioners, Area Teams, Strategic Clinical Networks). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, DH, NICE, AHSNs). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of reports can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced Health-IQs license agreement, which is signed between Health iQ and any client. 3. Public Access ‘Health iQ Insight’ Reports These are reports based on aggregated, suppressed, non-sensitive, non-identifiable HES data with the aim of: • Highlighting trends in demand and activity in a disease area. • Raising awareness of a disease area. • Providing high-level analysis of the management of a disease area. These reports are being made publically available, including being viewed on a dedicated area on the Health iQ website. The first of these (Care cost and activity in MS and Neurology in the Greater Manchester area) has been published.	All data processing is done within the UK, and is carried out according to the following process: 1. Data is received from HSCIC (via HSCIC’s secure FTP link), by either the Head of Delivery or Senior Project Manager 2. Data is uploaded by either Head of Delivery or Lead Developer via an encrypted external drive onto a secure local server (server is security protected and locally based in the head office). 3. Data is deleted from encrypted external drive 4. Calculations are run against the HES data 5. Calculated HES Data uploaded into secure local data warehouse 6. Data undergoes testing process 7. A subset of the HES data is exported from the secure local server, and uploaded via an encrypted connection into the Vantage backend system on external UK data centre hosted by UK Fast, who only are only the 'bricks and mortar' location and do not process data. 8. Aggregate data is made available through the Vantage presentation layer to the live system users who access Vantage via a secure password login system. 9. Health iQ analysts will access record level data via the local data warehouse and secure connection only. 10. Backup of the Vantage system is held on a dedicated, secure, private encrypted backup drive The Vantage system is held entirely on the secure server, and accessed only via secure web link (no record level data is held on any customer’s local machine at any time). The above processing means that the Vantage tool only presents aggregate data, and thus only aggregate data is available to customers of the tool. The HES data protection policy has been enforced as follows: 1. The record level data (pseudonymised, non-identifiable) will only be stored in secured local data warehouse, hardware encrypted disk (for in house backup) or secure vantage hosting environment in UK Data centre 2. All Health iQ staff are instructed not to download any record level HES data to local PCs, laptops or any non-encrypted device. 3. Health iQ developer and analyst teams use PC/ laptops with encrypted drives 4. All data transmission must be encrypted to minimise the risk. Pre-defined reports are exportable, in CSV and PDF formats. All reports are of aggregate data only. Users can create their own reports and export them. All staff who have access to the raw record level data are Health iQ staff, and this function is never outsourced to anyone else. Small numbers in the Vantage system and any other outputs are suppressed in line with the HES analysis guide, specifically :- - For Vantage tool: rounding of all patient and admission counts to nearest multiple of 5. Eg. If at provider level patient count is 34. This provider has only two hospitals A and B, where A has 30 patients and B has 4 patients. The tool will show provider level patient count as 30 and hospital level counts as 30 and 5 respectively. A user will never get a number lower than 5. Furthermore, since all the patient and admission counts have been rounded to nearest multiple of 5, user will never know exact patient or admission counts. - For other outputs: as per HES Analysis Guide Health-IQ’s team includes consultants and data specialists the majority of whom are former NHS employees, who have worked at senior levels in Commissioning, Performance and Information management functions. Health iQ have utilised this insight into the needs of NHS commissioning and provider organisations to design the Vantage tool. All individuals with access to record-level data are employees of Health iQ. Full data is required, as Health iQ's analysis is not limited to any particular age, region or any other sub-group. The Vantage hosting infrastructure is regularly penetration tested by an external independent vendor. The latest penetration testing by an external vendor has been confirmed and is scheduled to take place in early 2017.	With significant growth in the number of NHS clients using Vantage, two case studies of use of the Vantage system producing aggregate reports in the last 6 months are listed below: Case study 1: London based provider trust: The trust was conducting some research into clinical effectiveness of the Open-Angle Glaucoma pathway. They used Vantage to look into prescribing patterns at a CCG level, and compare with hospitalisation outcomes for diagnosed patients. They effectively replicated a study done in the US, which allowed them to make a robust case for changes in the pathway. Case study 2: Manchester based provider trust: The trust was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. They used Vantage to look at referrals for a range of diagnosis (eg Motor Neuron Disease) from different sources. They wanted to understand where the patients came from, what type of site they were treated in and what the main outcomes were. They then benchmarked all local trusts to see how the service model varied. It allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. Other examples of on-going uses of the data over the past 12 months are included below - as with all outputs, all of these will be small-number suppressed according to an agreed methodology (which at minimum ensures suppression in line with the HES Analysis Guide), aggregated, non-sensitive and non-identifiable: • Vantage data used to produce ‘Burden of Hospitalisation’ paper in Parkinson’s disease. This is now cited by Parkinson’s UK, and has also been quoted to support business cases for new Parkinson's nurses in a number of NHS Trusts (including Addenbrookes, Hertfordshire and Staffordshire) and to support new Parkinson’s pathway /guidelines standards (at Trusts such as Ipswich and Dudley). • The West Midlands Epilepsy SCN’s annual report on reducing epilepsy-related non- elective admissions in the locality. Other outputs not from the Vantage system will only contain data suppressed in line with the HES Analysis Guide, and will be ad hoc reports for the customers outlined in the Objectives. None of the outputs may be used for sales or marketing purposes by the Health-IQ customer.	• Vantage was used by a trust that was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. Their work allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. • The West Midlands Epilepsy SCN use Vantage on an ongoing basis to focus and review its main project of reducing non- elective admissions in the locality (see details at http://www.wmscnsenate.nhs.uk/strategic-clinical-networks/our-network/mental-health-dementia-and-neurological-conditions/current-projects/epilepsy/). Stated benefits have included (and are anticipated annually): - A West Midlands wide template care plan for epilepsy patients (March 2015) - Development of a care pathway to optimise care and reduce repeat admissions (March 2015) - Information pack provided to CCG commissioners (May 2015) - 5% Reduction in non elective admissions within 14 days where epilepsy is the primary reason for admission (Sept 2015) - 100% of patients presenting at A&E with a primary diagnosis of epilepsy will either be referred to a first seizure clinic or epilepsy specialist following a non-elective presentation. • Vantage data is used at Parkinson’s Excellence Networks and SCN's on an ongoing basis to review regional services and variation. The data continues to be presented to hospital departments and large scale regional meetings to demonstrate evidence of need to change. Health-IQ has provided three examples above of how the tool will be used, including the benefits to healthcare that will result from this use. These are typical of the type of usage our customers offer to the NHS. Health-IQ have listed some of the main benefits, and will provide (on renewal of the data) further examples of what specific benefits have been given through the use of the tool.
HEALTH IQ LTD	HEALTH IQ LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Health iQ is a provider of data-based insight, who produce tools and reports used by health and social care to achieve the following broad aims: 1. Understand and quantify the burden of disease. 2. Support service improvement in terms of treatment and efficiency of service. Health iQ wishes to provide such insight to Healthcare Providers and the Life Sciences industry. Health iQ will use the data solely for the following purposes: 1. Vantage System and Related Support Vantage is an online system that produces aggregated, small-number suppressed, non-sensitive, non-identifiable HES-based dashboards and reports to support the delivery of healthcare. It supports the delivery of a range of key healthcare strategic priorities, including delivering the Five-Year Forward-View, Quality, Innovation, Productivity and Prevention (QIPP) targets and Joint Strategic Needs Assessment (JSNA) targets. Vantage enables users to: • Plan healthcare provision with the support of real world data. • Benchmark performance against peer groups. • Pinpoint areas of inefficiency. • Validate the impact of a service improvement programme or new pathway model. The users of Vantage are limited to the following: 1. NHS users (Provider Trusts, GPs, Commissioners including new NHS commissioning organisations/collaborations such as Vanguards and STPs, Area Teams, Strategic Clinical Networks (SCNs)). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, Department of Health (DH), NICE, Academic Health Science Networks (AHSNs)). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of Vantage can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced through sub-license agreement between Health iQ and users. Life Science organisations are a user of Vantage exclusively for the purpose of providing benefit to healthcare. As with all user groups, they will only ever have access to aggregated outputs and are bound by sub-license agreements which ensure the usage of the data is in line with this document. In addition, Health iQ insist that all users of the tool undergo information governance training by a Health iQ trainer, and all reports produced by the tool come with a pre-written disclaimer statement. 2. Reports Health IQ will produce reports either as responses to specific data requests, or as part of wider projects. These reports will take the form of suppressed, aggregated, non-sensitive and non-identifiable data tables. As these reports will be constructed in response to a specific need, the content will vary, though all conform to all the restrictions outlined in this document. Examples of such reports could be: • A report by Hospital on total activity which falls within a Best-Practice Tariff (BPT) area, and the proportion of such activity which achieved the BPT. • A report of the tariff cost of Irritable Bowel Syndrome (IBS) patients by CCG, including all related symptoms and associated conditions to produce a ‘true burden’ analysis of the cost of IBD (Irritable Bowel Disease) to the healthcare system. To be absolutely clear, reports will never: • Relate or link HES data to the use of commercially available products, such as the prescribing of an individual pharmaceutical product. • Present data in a way which patient or clinician identity can be identified, even by linking to other datasets. • Break suppression rules. The potential users of reports are: 1. NHS users (Provider Trusts, GPs, Commissioners, Area Teams, Strategic Clinical Networks). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, DH, NICE, AHSNs). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of reports can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced Health-IQs license agreement, which is signed between Health iQ and any client. 3. Public Access ‘Health iQ Insight’ Reports These are reports based on aggregated, suppressed, non-sensitive, non-identifiable HES data with the aim of: • Highlighting trends in demand and activity in a disease area. • Raising awareness of a disease area. • Providing high-level analysis of the management of a disease area. These reports are being made publically available, including being viewed on a dedicated area on the Health iQ website. The first of these (Care cost and activity in MS and Neurology in the Greater Manchester area) has been published.	All data processing is done within the UK, and is carried out according to the following process: 1. Data is received from HSCIC (via HSCIC’s secure FTP link), by either the Head of Delivery or Senior Project Manager 2. Data is uploaded by either Head of Delivery or Lead Developer via an encrypted external drive onto a secure local server (server is security protected and locally based in the head office). 3. Data is deleted from encrypted external drive 4. Calculations are run against the HES data 5. Calculated HES Data uploaded into secure local data warehouse 6. Data undergoes testing process 7. A subset of the HES data is exported from the secure local server, and uploaded via an encrypted connection into the Vantage backend system on external UK data centre hosted by UK Fast, who only are only the 'bricks and mortar' location and do not process data. 8. Aggregate data is made available through the Vantage presentation layer to the live system users who access Vantage via a secure password login system. 9. Health iQ analysts will access record level data via the local data warehouse and secure connection only. 10. Backup of the Vantage system is held on a dedicated, secure, private encrypted backup drive The Vantage system is held entirely on the secure server, and accessed only via secure web link (no record level data is held on any customer’s local machine at any time). The above processing means that the Vantage tool only presents aggregate data, and thus only aggregate data is available to customers of the tool. The HES data protection policy has been enforced as follows: 1. The record level data (pseudonymised, non-identifiable) will only be stored in secured local data warehouse, hardware encrypted disk (for in house backup) or secure vantage hosting environment in UK Data centre 2. All Health iQ staff are instructed not to download any record level HES data to local PCs, laptops or any non-encrypted device. 3. Health iQ developer and analyst teams use PC/ laptops with encrypted drives 4. All data transmission must be encrypted to minimise the risk. Pre-defined reports are exportable, in CSV and PDF formats. All reports are of aggregate data only. Users can create their own reports and export them. All staff who have access to the raw record level data are Health iQ staff, and this function is never outsourced to anyone else. Small numbers in the Vantage system and any other outputs are suppressed in line with the HES analysis guide, specifically :- - For Vantage tool: rounding of all patient and admission counts to nearest multiple of 5. Eg. If at provider level patient count is 34. This provider has only two hospitals A and B, where A has 30 patients and B has 4 patients. The tool will show provider level patient count as 30 and hospital level counts as 30 and 5 respectively. A user will never get a number lower than 5. Furthermore, since all the patient and admission counts have been rounded to nearest multiple of 5, user will never know exact patient or admission counts. - For other outputs: as per HES Analysis Guide Health-IQ’s team includes consultants and data specialists the majority of whom are former NHS employees, who have worked at senior levels in Commissioning, Performance and Information management functions. Health iQ have utilised this insight into the needs of NHS commissioning and provider organisations to design the Vantage tool. All individuals with access to record-level data are employees of Health iQ. Full data is required, as Health iQ's analysis is not limited to any particular age, region or any other sub-group. The Vantage hosting infrastructure is regularly penetration tested by an external independent vendor. The latest penetration testing by an external vendor has been confirmed and is scheduled to take place in early 2017.	With significant growth in the number of NHS clients using Vantage, two case studies of use of the Vantage system producing aggregate reports in the last 6 months are listed below: Case study 1: London based provider trust: The trust was conducting some research into clinical effectiveness of the Open-Angle Glaucoma pathway. They used Vantage to look into prescribing patterns at a CCG level, and compare with hospitalisation outcomes for diagnosed patients. They effectively replicated a study done in the US, which allowed them to make a robust case for changes in the pathway. Case study 2: Manchester based provider trust: The trust was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. They used Vantage to look at referrals for a range of diagnosis (eg Motor Neuron Disease) from different sources. They wanted to understand where the patients came from, what type of site they were treated in and what the main outcomes were. They then benchmarked all local trusts to see how the service model varied. It allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. Other examples of on-going uses of the data over the past 12 months are included below - as with all outputs, all of these will be small-number suppressed according to an agreed methodology (which at minimum ensures suppression in line with the HES Analysis Guide), aggregated, non-sensitive and non-identifiable: • Vantage data used to produce ‘Burden of Hospitalisation’ paper in Parkinson’s disease. This is now cited by Parkinson’s UK, and has also been quoted to support business cases for new Parkinson's nurses in a number of NHS Trusts (including Addenbrookes, Hertfordshire and Staffordshire) and to support new Parkinson’s pathway /guidelines standards (at Trusts such as Ipswich and Dudley). • The West Midlands Epilepsy SCN’s annual report on reducing epilepsy-related non- elective admissions in the locality. Other outputs not from the Vantage system will only contain data suppressed in line with the HES Analysis Guide, and will be ad hoc reports for the customers outlined in the Objectives. None of the outputs may be used for sales or marketing purposes by the Health-IQ customer.	• Vantage was used by a trust that was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. Their work allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. • The West Midlands Epilepsy SCN use Vantage on an ongoing basis to focus and review its main project of reducing non- elective admissions in the locality (see details at http://www.wmscnsenate.nhs.uk/strategic-clinical-networks/our-network/mental-health-dementia-and-neurological-conditions/current-projects/epilepsy/). Stated benefits have included (and are anticipated annually): - A West Midlands wide template care plan for epilepsy patients (March 2015) - Development of a care pathway to optimise care and reduce repeat admissions (March 2015) - Information pack provided to CCG commissioners (May 2015) - 5% Reduction in non elective admissions within 14 days where epilepsy is the primary reason for admission (Sept 2015) - 100% of patients presenting at A&E with a primary diagnosis of epilepsy will either be referred to a first seizure clinic or epilepsy specialist following a non-elective presentation. • Vantage data is used at Parkinson’s Excellence Networks and SCN's on an ongoing basis to review regional services and variation. The data continues to be presented to hospital departments and large scale regional meetings to demonstrate evidence of need to change. Health-IQ has provided three examples above of how the tool will be used, including the benefits to healthcare that will result from this use. These are typical of the type of usage our customers offer to the NHS. Health-IQ have listed some of the main benefits, and will provide (on renewal of the data) further examples of what specific benefits have been given through the use of the tool.
HEALTH IQ LTD	HEALTH IQ LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Health iQ is a provider of data-based insight, who produce tools and reports used by health and social care to achieve the following broad aims: 1. Understand and quantify the burden of disease. 2. Support service improvement in terms of treatment and efficiency of service. Health iQ wishes to provide such insight to Healthcare Providers and the Life Sciences industry. Health iQ will use the data solely for the following purposes: 1. Vantage System and Related Support Vantage is an online system that produces aggregated, small-number suppressed, non-sensitive, non-identifiable HES-based dashboards and reports to support the delivery of healthcare. It supports the delivery of a range of key healthcare strategic priorities, including delivering the Five-Year Forward-View, Quality, Innovation, Productivity and Prevention (QIPP) targets and Joint Strategic Needs Assessment (JSNA) targets. Vantage enables users to: • Plan healthcare provision with the support of real world data. • Benchmark performance against peer groups. • Pinpoint areas of inefficiency. • Validate the impact of a service improvement programme or new pathway model. The users of Vantage are limited to the following: 1. NHS users (Provider Trusts, GPs, Commissioners including new NHS commissioning organisations/collaborations such as Vanguards and STPs, Area Teams, Strategic Clinical Networks (SCNs)). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, Department of Health (DH), NICE, Academic Health Science Networks (AHSNs)). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of Vantage can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced through sub-license agreement between Health iQ and users. Life Science organisations are a user of Vantage exclusively for the purpose of providing benefit to healthcare. As with all user groups, they will only ever have access to aggregated outputs and are bound by sub-license agreements which ensure the usage of the data is in line with this document. In addition, Health iQ insist that all users of the tool undergo information governance training by a Health iQ trainer, and all reports produced by the tool come with a pre-written disclaimer statement. 2. Reports Health IQ will produce reports either as responses to specific data requests, or as part of wider projects. These reports will take the form of suppressed, aggregated, non-sensitive and non-identifiable data tables. As these reports will be constructed in response to a specific need, the content will vary, though all conform to all the restrictions outlined in this document. Examples of such reports could be: • A report by Hospital on total activity which falls within a Best-Practice Tariff (BPT) area, and the proportion of such activity which achieved the BPT. • A report of the tariff cost of Irritable Bowel Syndrome (IBS) patients by CCG, including all related symptoms and associated conditions to produce a ‘true burden’ analysis of the cost of IBD (Irritable Bowel Disease) to the healthcare system. To be absolutely clear, reports will never: • Relate or link HES data to the use of commercially available products, such as the prescribing of an individual pharmaceutical product. • Present data in a way which patient or clinician identity can be identified, even by linking to other datasets. • Break suppression rules. The potential users of reports are: 1. NHS users (Provider Trusts, GPs, Commissioners, Area Teams, Strategic Clinical Networks). 2. Commissioning Support Units (CSUs). 3. Governmental organisations (NHS England, DH, NICE, AHSNs). 4. Social care (Local Authorities, Health & Wellbeing Boards). 5. Charities and not-for-profit organisations. 6. Life Sciences organisations (Pharmaceuticals, Medical Technology, Biotechnology). Though the users of reports can be from any of the above listed groups, it is made clear that the allowed purposes of use are restricted to those mentioned in this document, with the ultimate beneficiary being healthcare as a whole. This is enforced Health-IQs license agreement, which is signed between Health iQ and any client. 3. Public Access ‘Health iQ Insight’ Reports These are reports based on aggregated, suppressed, non-sensitive, non-identifiable HES data with the aim of: • Highlighting trends in demand and activity in a disease area. • Raising awareness of a disease area. • Providing high-level analysis of the management of a disease area. These reports are being made publically available, including being viewed on a dedicated area on the Health iQ website. The first of these (Care cost and activity in MS and Neurology in the Greater Manchester area) has been published.	All data processing is done within the UK, and is carried out according to the following process: 1. Data is received from HSCIC (via HSCIC’s secure FTP link), by either the Head of Delivery or Senior Project Manager 2. Data is uploaded by either Head of Delivery or Lead Developer via an encrypted external drive onto a secure local server (server is security protected and locally based in the head office). 3. Data is deleted from encrypted external drive 4. Calculations are run against the HES data 5. Calculated HES Data uploaded into secure local data warehouse 6. Data undergoes testing process 7. A subset of the HES data is exported from the secure local server, and uploaded via an encrypted connection into the Vantage backend system on external UK data centre hosted by UK Fast, who only are only the 'bricks and mortar' location and do not process data. 8. Aggregate data is made available through the Vantage presentation layer to the live system users who access Vantage via a secure password login system. 9. Health iQ analysts will access record level data via the local data warehouse and secure connection only. 10. Backup of the Vantage system is held on a dedicated, secure, private encrypted backup drive The Vantage system is held entirely on the secure server, and accessed only via secure web link (no record level data is held on any customer’s local machine at any time). The above processing means that the Vantage tool only presents aggregate data, and thus only aggregate data is available to customers of the tool. The HES data protection policy has been enforced as follows: 1. The record level data (pseudonymised, non-identifiable) will only be stored in secured local data warehouse, hardware encrypted disk (for in house backup) or secure vantage hosting environment in UK Data centre 2. All Health iQ staff are instructed not to download any record level HES data to local PCs, laptops or any non-encrypted device. 3. Health iQ developer and analyst teams use PC/ laptops with encrypted drives 4. All data transmission must be encrypted to minimise the risk. Pre-defined reports are exportable, in CSV and PDF formats. All reports are of aggregate data only. Users can create their own reports and export them. All staff who have access to the raw record level data are Health iQ staff, and this function is never outsourced to anyone else. Small numbers in the Vantage system and any other outputs are suppressed in line with the HES analysis guide, specifically :- - For Vantage tool: rounding of all patient and admission counts to nearest multiple of 5. Eg. If at provider level patient count is 34. This provider has only two hospitals A and B, where A has 30 patients and B has 4 patients. The tool will show provider level patient count as 30 and hospital level counts as 30 and 5 respectively. A user will never get a number lower than 5. Furthermore, since all the patient and admission counts have been rounded to nearest multiple of 5, user will never know exact patient or admission counts. - For other outputs: as per HES Analysis Guide Health-IQ’s team includes consultants and data specialists the majority of whom are former NHS employees, who have worked at senior levels in Commissioning, Performance and Information management functions. Health iQ have utilised this insight into the needs of NHS commissioning and provider organisations to design the Vantage tool. All individuals with access to record-level data are employees of Health iQ. Full data is required, as Health iQ's analysis is not limited to any particular age, region or any other sub-group. The Vantage hosting infrastructure is regularly penetration tested by an external independent vendor. The latest penetration testing by an external vendor has been confirmed and is scheduled to take place in early 2017.	With significant growth in the number of NHS clients using Vantage, two case studies of use of the Vantage system producing aggregate reports in the last 6 months are listed below: Case study 1: London based provider trust: The trust was conducting some research into clinical effectiveness of the Open-Angle Glaucoma pathway. They used Vantage to look into prescribing patterns at a CCG level, and compare with hospitalisation outcomes for diagnosed patients. They effectively replicated a study done in the US, which allowed them to make a robust case for changes in the pathway. Case study 2: Manchester based provider trust: The trust was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. They used Vantage to look at referrals for a range of diagnosis (eg Motor Neuron Disease) from different sources. They wanted to understand where the patients came from, what type of site they were treated in and what the main outcomes were. They then benchmarked all local trusts to see how the service model varied. It allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. Other examples of on-going uses of the data over the past 12 months are included below - as with all outputs, all of these will be small-number suppressed according to an agreed methodology (which at minimum ensures suppression in line with the HES Analysis Guide), aggregated, non-sensitive and non-identifiable: • Vantage data used to produce ‘Burden of Hospitalisation’ paper in Parkinson’s disease. This is now cited by Parkinson’s UK, and has also been quoted to support business cases for new Parkinson's nurses in a number of NHS Trusts (including Addenbrookes, Hertfordshire and Staffordshire) and to support new Parkinson’s pathway /guidelines standards (at Trusts such as Ipswich and Dudley). • The West Midlands Epilepsy SCN’s annual report on reducing epilepsy-related non- elective admissions in the locality. Other outputs not from the Vantage system will only contain data suppressed in line with the HES Analysis Guide, and will be ad hoc reports for the customers outlined in the Objectives. None of the outputs may be used for sales or marketing purposes by the Health-IQ customer.	• Vantage was used by a trust that was conducting a service evaluation in order to support a case for change to optimise the pathway in Neuroscience. Their work allowed them to understand the variation of care in the region, and make recommendations to standardise care to an optimal pathway across the region and develop a more integrated care pathway. • The West Midlands Epilepsy SCN use Vantage on an ongoing basis to focus and review its main project of reducing non- elective admissions in the locality (see details at http://www.wmscnsenate.nhs.uk/strategic-clinical-networks/our-network/mental-health-dementia-and-neurological-conditions/current-projects/epilepsy/). Stated benefits have included (and are anticipated annually): - A West Midlands wide template care plan for epilepsy patients (March 2015) - Development of a care pathway to optimise care and reduce repeat admissions (March 2015) - Information pack provided to CCG commissioners (May 2015) - 5% Reduction in non elective admissions within 14 days where epilepsy is the primary reason for admission (Sept 2015) - 100% of patients presenting at A&E with a primary diagnosis of epilepsy will either be referred to a first seizure clinic or epilepsy specialist following a non-elective presentation. • Vantage data is used at Parkinson’s Excellence Networks and SCN's on an ongoing basis to review regional services and variation. The data continues to be presented to hospital departments and large scale regional meetings to demonstrate evidence of need to change. Health-IQ has provided three examples above of how the tool will be used, including the benefits to healthcare that will result from this use. These are typical of the type of usage our customers offer to the NHS. Health-IQ have listed some of the main benefits, and will provide (on renewal of the data) further examples of what specific benefits have been given through the use of the tool.
HMRC	HMRC	GP Census data at individual level for all 4 Countries (England, Wales, Scotland and NI)	Identifiable	Non Sensitive	Section 36, Health Act 2009	Ongoing	N	GP Census data at individual level for all 4 Countries (England, Wales, Scotland and NI) supplied to HMRC to link to their Tax Return data to supply an anonymised aggregated Earnings data set back to HSCIC Workforce.
I5 HEALTH	I5 HEALTH	Bespoke Monthly Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	i5 Health Limited (i5 Health) requires health data for the following purposes: Purpose #1) i5 Health Limited (i5 Health) evaluates, on behalf of the Health Education Board of NHS England, the economic impact of Non-Medical Prescribing (NMP) - the prescribing of drugs by health practitioners other than doctors. i5 Health analyses the relevant activity data in order to identify utilisation of NMP practitioners in various healthcare settings. In doing so, they can measure the impact NMP has or, if introduced more widely, will have on different health economies. (Academic Paper ID: WNC 48 'Nurse Prescribing' - WorldwideNursingConference,Singapore2014;abstract http://www.citeulike.org/user/gstf/article/13247895 ) First full first report (http://www.i5health.com/NMP/NMPEconomicEvaluation.pdf ) to be updated when i5 Health receives 2016/17 data. Purpose #2) i5 Health provides consultancy services to support to Clinical Commissioning Groups (CCG), CSUs, Sustainability and Transformation Plans (STP), Acutes, NHS England and Local Authorities (LA) in their decision making for commissioning purposes. The specific purposes are:- Purpose #2.1) To identify realistic NHS Quality, Innovation, Productivity and Prevention (QIPP) QIPP initiatives for specific CCGs, Commissioning Support Units (CSU) and Providers in order to spot trends and to perform benchmarking that support commissioners in particular with their operational, strategic planning and co-commissioning. Current work includes with NHS England to identify suitable initiatives for Specialist Services like Cardiology and Cardiac Surgery. It also includes provision of patient counts for Long Term Conditions (LTC) to GPs to enable them to evaluate the quality of their Quality Outcome Framework (QOF) registers and devise appropriate actions (with small numbers suppressed). Purpose #2.2) i5 Health advises Voluntary Sector Organisations (VSOs) that have charitable status and exist to complement the work of the NHS in improving patient care. Such VSOs include Age UK and Asthma UK. Only voluntary organisations that are commissioned by the NHS will be clients of this service. Purpose #2.3) To measure standards of care and identify gaps in provision to inform commissioning strategy. A number of CCGs including NHS Halton CCG, C4G CCG, Brent CCG, Ashford CCG, have been working with i5 Health in this respect to develop their strategies. Where NHS Digital has already given formal approval for i5 Health to analyse data (IG Ref DSCON066/Halton CCG), the outcome was described by the Director of Transformation as giving; "…..Halton CCG a unique glance into what financial results could be made through our partnership approach. Unlike any other piece of consultancy, i5 and COP shone an economic light on what schemes are working well and what areas i5 Health could prioritise our energy on." i5 Health requires SUS PBR spells & episode at patient level, including procedure and diagnosis codes, in order to evaluate the applicability of a particular QIPP initiative for a group of patients. Data on PBR spells and episodes is essential in i5 Health establishing the nature and size of specific patient cohorts in a given acute provider setting. Such identification allows i5 Health to calculate accurately the effect of any proposed, specific initiative including the financial impact of that change (e.g. provision of certain alternatives in the primary care sector to hospital treatment). VSOs already cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients.	NHS Digital will provide i5Health with record level pseudo/anonymised SUS PbR data via the Secure Electronic File Transfer (SEFT) system. A Database Analyst (DBA) from i5Health will load the record level data into a database. The database will be managed locally and secured by the DBA with user access control. i5 Health will be using SQL Server 2008 on a bit locker encrypted partition. Record-level data will only be accessed by individuals within the Analytics Team, who have the authorisation from the Operations Director (who is also Caldicott Guardian), to access the data for the purpose (s) described, all of whom are substantive employees of i5 Health. Data will only be accessed at the named processing location as set out in this application. The additional SUS PbR Data being provided will be linked to SUS PbR data already held, across the datasets (e.g. SUS PbR Episode data with SUS PbR A&E data); from National Level to GP Practice Level. There will be no requirement nor attempt to re-identify individuals within the datasets. National data is required as i5 Health provide reports from local through Regional to National levels. Multiple years of data are required in order to produce time-series and predictive modelling, historic data is retained to enable this. The data cannot be minimised by applying filters to specific conditions of relevance as the full data is needed in order to produce the outputs as outlined within the application. Data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. For example, a report containing aggregate data for cohorts from localities to large geographical areas will be produced for NHS England. The inclusion of Voluntary Sector Organisations (VSO) as recipients in the ‘commercial’ purpose context outlined below in no way changes the processing activities set out in this paragraph, but will be limited to aggregate data.	The outputs will be aggregated analysis with small numbers suppressed for inclusion within economic evaluation and Clinical Commissioning Group (CCG) strategy. All outputs are solely provided to the NHS customers and no service/product/data will be supplied to any commercial organisation by i5 Health except in so far as is permitted for Voluntary Sector Organisations (VSO) for the purpose outlined in the ‘commercial’ purpose section below. The data provided will be used solely for the purposes identified above. The outputs i5 Health Limited have provided over the last 24 months are (according to each purpose); Purpose #1) Non-Medical Prescribing • Creation of first national report for Health Education England on the economic value of Non-Medical Prescribing called on three categories of HSCIC information (latest HES data in respect of long term health conditions (LTC); Nurses currently in the workforce, and Nurses using FP10 Prescription forms). Purpose 2) Consultancy Service • A number of commissioning support reports are made for CCGs within the footprint of Business Intelligence partners, Arden & GEM CSU; NHS England has missioned i5 Health to provide commissioning reports for 32 CCGs and 5 Sustainability and Transformation Plans (STP) in London as well as analysis of the effect on London over the next five years of the introduction of Digital technology;. Purpose #2.1) End of Life • As part of NHS England’s Electronic Palliative Care Co-ordination System (EPaCCS) programme, i5 Health Limited carried out an evaluation of data in respect of End of Life and its related costs. This is a continuing project – the outcomes being dependent on changes in trends of data. The studies have been carried out at the specific requests of the various CCGs within England and Wales. Purpose #2.2) Case Finding • In many parts of the country, a number of LTC patients are sub-optimally treated because they fail to get on to the relevant registers at GP practices; additionally, there are many patients that have conditions which, if identified early enough, could receive treatment that reduces the risk of them progressing to a full LTC. i5 Health has developed algorithms that identify, at surgery level, the numbers of patients that fall into both these categories. NHS England requires i5 Health to carry out a study in respect of the GP practices in Southport and Formby. This NHS England initiative is continuing. Purpose #2.3) Urgent Care • Halton CCG needed to map out the Urgent Care pressure across all the supporting Hospitals. Linked with this exercise, they commissioned i5 Health, using the Commissioning Opportunity module (COP), to investigate the patient urgent care journey. (i5 Health Limited are now discussing applying the same skills for the benefit of other North West CCGs including South Sefton CCG and Southport and Formby CCG). Purpose #2.3) Readmissions • Halton CCG asked for the assistance of i5 Health in analysing significant Readmissions issues. The analysis, based on the COP algorithms, got right to the heart of the problem and identified a significant number of patients that, under normal circumstances, should not have undergone readmission. Purpose #2.3) Outpatient Procedures • On behalf of Halton CCG, More recently, i5 Health performed analysis of into what has been happening in respect of Outpatient episodes and then developed some solutions to excessive use in Cardiology, Mouth/Head/Neck & Ears, Orthopaedic Non-Trauma and Urology. Purpose #2.3) ACS – Respiratory and Ear, Nose, Throat (ENT) • i5 Health established what might, currently, be the best opportunity for Halton CCG to reduce acute care activity and cost - dealing with Respiratory and Ear Nose Throat conditions. Besides analysing historic and current situation, i5 Health examined six case studies to establish, using a Population Health Management approach, what might be optimum strategies for to pursue (the product of this work is now being leveraged into the Case Finding activity).	The Non-Medical Prescribing (NMP) has been in existence for 26 years. Over time there are good reasons to believe that the returns from it, across the board, have been very positive: from cost effectiveness, through staff development to patient satisfaction. Not least of all, the clinicians Audit, in growing use since 2009, has elicited important data supportive of that contention. However, greater evidence of the performance and effect of NMP is necessary. i5 Health is therefore being asked to review existing HES and studies to draw out relevant information, propose new methodology, refine existing audits and promote new ones to provide a comprehensive analysis of NMP to assist in decisions on whether and to what extent NMP should be adopted more widely in England. Voluntary Sector Organisations (VSO) cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients. The benefits to the UK Health and Social Care system are better strategic planning and commissioning decisions, and subsequently improved care for patients due to better planning and strategy. The financial benefit for the healthcare system varies from case to case. By way of example, analysis by i5 Health for the Sussex health economy two years ago resulted in a 10% reduction in Non-Elective admissions (NEL) thus saving hundreds of thousands of pounds annually. Benefits achieved in the last 10 months; Purpose #1) Non-Medical Prescribing • With increasing pressure on the availability of doctors in both primary and secondary care, there is a growing case for greater use of NMP i.e. prescribing by a non-doctor (e.g. nurse, pharmacist, etc..). That case is reinforced by the cost/benefit identified and the better levels of care demonstrated. Purpose #2.1) End of Life • The HES based studies so far are showing a disturbing picture particularly, though not exclusively, in respect of the frail and elderly in their last year of life. The concerns are around the high levels of admissions to hospitals and the distress to patients this causes. Further work needs to be done for some CCGs on identifying, with business cases, the alternative strategies that can answer the above concerns. Purpose #2.1) Case Finding • Identification of patients that have (or risk having) an Long Term Condition (LTC) but do not appear on the GPs risk register can lead to better management of their health requirements. Purpose #2.3) Urgent Care • Commissioning Opportunity (COP) is all about matching patient groups against successful healthcare initiatives and forecasting the effect of local implementation on patient care and budgets. Purpose #2.3) Readmissions • Issues relating to specific surgeons were highlighted. i5 Health went beyond problem identification and evaluation and made detailed and well considered recommendations – not just for the CCG but also its providers and colleagues across primary and community care. Purpose #2.3) Outpatients • The i5 Health solutions included alternatives for 6,000 procedures currently costing over £1.5m. The solutions are being implemented and will result in significant cost savings. Purpose #2.3) ACS - Respiratory and ENT • One of the many positive outcomes of the exercise has been the identification of over 200 patients clinically diagnosed with COPD in secondary care that are not on the GPs risk register and which are likely to be unmanaged.
I5 HEALTH	I5 HEALTH	Bespoke Monthly Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	i5 Health Limited (i5 Health) requires health data for the following purposes: Purpose #1) i5 Health Limited (i5 Health) evaluates, on behalf of the Health Education Board of NHS England, the economic impact of Non-Medical Prescribing (NMP) - the prescribing of drugs by health practitioners other than doctors. i5 Health analyses the relevant activity data in order to identify utilisation of NMP practitioners in various healthcare settings. In doing so, they can measure the impact NMP has or, if introduced more widely, will have on different health economies. (Academic Paper ID: WNC 48 'Nurse Prescribing' - WorldwideNursingConference,Singapore2014;abstract http://www.citeulike.org/user/gstf/article/13247895 ) First full first report (http://www.i5health.com/NMP/NMPEconomicEvaluation.pdf ) to be updated when i5 Health receives 2016/17 data. Purpose #2) i5 Health provides consultancy services to support to Clinical Commissioning Groups (CCG), CSUs, Sustainability and Transformation Plans (STP), Acutes, NHS England and Local Authorities (LA) in their decision making for commissioning purposes. The specific purposes are:- Purpose #2.1) To identify realistic NHS Quality, Innovation, Productivity and Prevention (QIPP) QIPP initiatives for specific CCGs, Commissioning Support Units (CSU) and Providers in order to spot trends and to perform benchmarking that support commissioners in particular with their operational, strategic planning and co-commissioning. Current work includes with NHS England to identify suitable initiatives for Specialist Services like Cardiology and Cardiac Surgery. It also includes provision of patient counts for Long Term Conditions (LTC) to GPs to enable them to evaluate the quality of their Quality Outcome Framework (QOF) registers and devise appropriate actions (with small numbers suppressed). Purpose #2.2) i5 Health advises Voluntary Sector Organisations (VSOs) that have charitable status and exist to complement the work of the NHS in improving patient care. Such VSOs include Age UK and Asthma UK. Only voluntary organisations that are commissioned by the NHS will be clients of this service. Purpose #2.3) To measure standards of care and identify gaps in provision to inform commissioning strategy. A number of CCGs including NHS Halton CCG, C4G CCG, Brent CCG, Ashford CCG, have been working with i5 Health in this respect to develop their strategies. Where NHS Digital has already given formal approval for i5 Health to analyse data (IG Ref DSCON066/Halton CCG), the outcome was described by the Director of Transformation as giving; "…..Halton CCG a unique glance into what financial results could be made through our partnership approach. Unlike any other piece of consultancy, i5 and COP shone an economic light on what schemes are working well and what areas i5 Health could prioritise our energy on." i5 Health requires SUS PBR spells & episode at patient level, including procedure and diagnosis codes, in order to evaluate the applicability of a particular QIPP initiative for a group of patients. Data on PBR spells and episodes is essential in i5 Health establishing the nature and size of specific patient cohorts in a given acute provider setting. Such identification allows i5 Health to calculate accurately the effect of any proposed, specific initiative including the financial impact of that change (e.g. provision of certain alternatives in the primary care sector to hospital treatment). VSOs already cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients.	NHS Digital will provide i5Health with record level pseudo/anonymised SUS PbR data via the Secure Electronic File Transfer (SEFT) system. A Database Analyst (DBA) from i5Health will load the record level data into a database. The database will be managed locally and secured by the DBA with user access control. i5 Health will be using SQL Server 2008 on a bit locker encrypted partition. Record-level data will only be accessed by individuals within the Analytics Team, who have the authorisation from the Operations Director (who is also Caldicott Guardian), to access the data for the purpose (s) described, all of whom are substantive employees of i5 Health. Data will only be accessed at the named processing location as set out in this application. The additional SUS PbR Data being provided will be linked to SUS PbR data already held, across the datasets (e.g. SUS PbR Episode data with SUS PbR A&E data); from National Level to GP Practice Level. There will be no requirement nor attempt to re-identify individuals within the datasets. National data is required as i5 Health provide reports from local through Regional to National levels. Multiple years of data are required in order to produce time-series and predictive modelling, historic data is retained to enable this. The data cannot be minimised by applying filters to specific conditions of relevance as the full data is needed in order to produce the outputs as outlined within the application. Data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. For example, a report containing aggregate data for cohorts from localities to large geographical areas will be produced for NHS England. The inclusion of Voluntary Sector Organisations (VSO) as recipients in the ‘commercial’ purpose context outlined below in no way changes the processing activities set out in this paragraph, but will be limited to aggregate data.	The outputs will be aggregated analysis with small numbers suppressed for inclusion within economic evaluation and Clinical Commissioning Group (CCG) strategy. All outputs are solely provided to the NHS customers and no service/product/data will be supplied to any commercial organisation by i5 Health except in so far as is permitted for Voluntary Sector Organisations (VSO) for the purpose outlined in the ‘commercial’ purpose section below. The data provided will be used solely for the purposes identified above. The outputs i5 Health Limited have provided over the last 24 months are (according to each purpose); Purpose #1) Non-Medical Prescribing • Creation of first national report for Health Education England on the economic value of Non-Medical Prescribing called on three categories of HSCIC information (latest HES data in respect of long term health conditions (LTC); Nurses currently in the workforce, and Nurses using FP10 Prescription forms). Purpose 2) Consultancy Service • A number of commissioning support reports are made for CCGs within the footprint of Business Intelligence partners, Arden & GEM CSU; NHS England has missioned i5 Health to provide commissioning reports for 32 CCGs and 5 Sustainability and Transformation Plans (STP) in London as well as analysis of the effect on London over the next five years of the introduction of Digital technology;. Purpose #2.1) End of Life • As part of NHS England’s Electronic Palliative Care Co-ordination System (EPaCCS) programme, i5 Health Limited carried out an evaluation of data in respect of End of Life and its related costs. This is a continuing project – the outcomes being dependent on changes in trends of data. The studies have been carried out at the specific requests of the various CCGs within England and Wales. Purpose #2.2) Case Finding • In many parts of the country, a number of LTC patients are sub-optimally treated because they fail to get on to the relevant registers at GP practices; additionally, there are many patients that have conditions which, if identified early enough, could receive treatment that reduces the risk of them progressing to a full LTC. i5 Health has developed algorithms that identify, at surgery level, the numbers of patients that fall into both these categories. NHS England requires i5 Health to carry out a study in respect of the GP practices in Southport and Formby. This NHS England initiative is continuing. Purpose #2.3) Urgent Care • Halton CCG needed to map out the Urgent Care pressure across all the supporting Hospitals. Linked with this exercise, they commissioned i5 Health, using the Commissioning Opportunity module (COP), to investigate the patient urgent care journey. (i5 Health Limited are now discussing applying the same skills for the benefit of other North West CCGs including South Sefton CCG and Southport and Formby CCG). Purpose #2.3) Readmissions • Halton CCG asked for the assistance of i5 Health in analysing significant Readmissions issues. The analysis, based on the COP algorithms, got right to the heart of the problem and identified a significant number of patients that, under normal circumstances, should not have undergone readmission. Purpose #2.3) Outpatient Procedures • On behalf of Halton CCG, More recently, i5 Health performed analysis of into what has been happening in respect of Outpatient episodes and then developed some solutions to excessive use in Cardiology, Mouth/Head/Neck & Ears, Orthopaedic Non-Trauma and Urology. Purpose #2.3) ACS – Respiratory and Ear, Nose, Throat (ENT) • i5 Health established what might, currently, be the best opportunity for Halton CCG to reduce acute care activity and cost - dealing with Respiratory and Ear Nose Throat conditions. Besides analysing historic and current situation, i5 Health examined six case studies to establish, using a Population Health Management approach, what might be optimum strategies for to pursue (the product of this work is now being leveraged into the Case Finding activity).	The Non-Medical Prescribing (NMP) has been in existence for 26 years. Over time there are good reasons to believe that the returns from it, across the board, have been very positive: from cost effectiveness, through staff development to patient satisfaction. Not least of all, the clinicians Audit, in growing use since 2009, has elicited important data supportive of that contention. However, greater evidence of the performance and effect of NMP is necessary. i5 Health is therefore being asked to review existing HES and studies to draw out relevant information, propose new methodology, refine existing audits and promote new ones to provide a comprehensive analysis of NMP to assist in decisions on whether and to what extent NMP should be adopted more widely in England. Voluntary Sector Organisations (VSO) cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients. The benefits to the UK Health and Social Care system are better strategic planning and commissioning decisions, and subsequently improved care for patients due to better planning and strategy. The financial benefit for the healthcare system varies from case to case. By way of example, analysis by i5 Health for the Sussex health economy two years ago resulted in a 10% reduction in Non-Elective admissions (NEL) thus saving hundreds of thousands of pounds annually. Benefits achieved in the last 10 months; Purpose #1) Non-Medical Prescribing • With increasing pressure on the availability of doctors in both primary and secondary care, there is a growing case for greater use of NMP i.e. prescribing by a non-doctor (e.g. nurse, pharmacist, etc..). That case is reinforced by the cost/benefit identified and the better levels of care demonstrated. Purpose #2.1) End of Life • The HES based studies so far are showing a disturbing picture particularly, though not exclusively, in respect of the frail and elderly in their last year of life. The concerns are around the high levels of admissions to hospitals and the distress to patients this causes. Further work needs to be done for some CCGs on identifying, with business cases, the alternative strategies that can answer the above concerns. Purpose #2.1) Case Finding • Identification of patients that have (or risk having) an Long Term Condition (LTC) but do not appear on the GPs risk register can lead to better management of their health requirements. Purpose #2.3) Urgent Care • Commissioning Opportunity (COP) is all about matching patient groups against successful healthcare initiatives and forecasting the effect of local implementation on patient care and budgets. Purpose #2.3) Readmissions • Issues relating to specific surgeons were highlighted. i5 Health went beyond problem identification and evaluation and made detailed and well considered recommendations – not just for the CCG but also its providers and colleagues across primary and community care. Purpose #2.3) Outpatients • The i5 Health solutions included alternatives for 6,000 procedures currently costing over £1.5m. The solutions are being implemented and will result in significant cost savings. Purpose #2.3) ACS - Respiratory and ENT • One of the many positive outcomes of the exercise has been the identification of over 200 patients clinically diagnosed with COPD in secondary care that are not on the GPs risk register and which are likely to be unmanaged.
I5 HEALTH	I5 HEALTH	Bespoke Monthly Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	i5 Health Limited (i5 Health) requires health data for the following purposes: Purpose #1) i5 Health Limited (i5 Health) evaluates, on behalf of the Health Education Board of NHS England, the economic impact of Non-Medical Prescribing (NMP) - the prescribing of drugs by health practitioners other than doctors. i5 Health analyses the relevant activity data in order to identify utilisation of NMP practitioners in various healthcare settings. In doing so, they can measure the impact NMP has or, if introduced more widely, will have on different health economies. (Academic Paper ID: WNC 48 'Nurse Prescribing' - WorldwideNursingConference,Singapore2014;abstract http://www.citeulike.org/user/gstf/article/13247895 ) First full first report (http://www.i5health.com/NMP/NMPEconomicEvaluation.pdf ) to be updated when i5 Health receives 2016/17 data. Purpose #2) i5 Health provides consultancy services to support to Clinical Commissioning Groups (CCG), CSUs, Sustainability and Transformation Plans (STP), Acutes, NHS England and Local Authorities (LA) in their decision making for commissioning purposes. The specific purposes are:- Purpose #2.1) To identify realistic NHS Quality, Innovation, Productivity and Prevention (QIPP) QIPP initiatives for specific CCGs, Commissioning Support Units (CSU) and Providers in order to spot trends and to perform benchmarking that support commissioners in particular with their operational, strategic planning and co-commissioning. Current work includes with NHS England to identify suitable initiatives for Specialist Services like Cardiology and Cardiac Surgery. It also includes provision of patient counts for Long Term Conditions (LTC) to GPs to enable them to evaluate the quality of their Quality Outcome Framework (QOF) registers and devise appropriate actions (with small numbers suppressed). Purpose #2.2) i5 Health advises Voluntary Sector Organisations (VSOs) that have charitable status and exist to complement the work of the NHS in improving patient care. Such VSOs include Age UK and Asthma UK. Only voluntary organisations that are commissioned by the NHS will be clients of this service. Purpose #2.3) To measure standards of care and identify gaps in provision to inform commissioning strategy. A number of CCGs including NHS Halton CCG, C4G CCG, Brent CCG, Ashford CCG, have been working with i5 Health in this respect to develop their strategies. Where NHS Digital has already given formal approval for i5 Health to analyse data (IG Ref DSCON066/Halton CCG), the outcome was described by the Director of Transformation as giving; "…..Halton CCG a unique glance into what financial results could be made through our partnership approach. Unlike any other piece of consultancy, i5 and COP shone an economic light on what schemes are working well and what areas i5 Health could prioritise our energy on." i5 Health requires SUS PBR spells & episode at patient level, including procedure and diagnosis codes, in order to evaluate the applicability of a particular QIPP initiative for a group of patients. Data on PBR spells and episodes is essential in i5 Health establishing the nature and size of specific patient cohorts in a given acute provider setting. Such identification allows i5 Health to calculate accurately the effect of any proposed, specific initiative including the financial impact of that change (e.g. provision of certain alternatives in the primary care sector to hospital treatment). VSOs already cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients.	NHS Digital will provide i5Health with record level pseudo/anonymised SUS PbR data via the Secure Electronic File Transfer (SEFT) system. A Database Analyst (DBA) from i5Health will load the record level data into a database. The database will be managed locally and secured by the DBA with user access control. i5 Health will be using SQL Server 2008 on a bit locker encrypted partition. Record-level data will only be accessed by individuals within the Analytics Team, who have the authorisation from the Operations Director (who is also Caldicott Guardian), to access the data for the purpose (s) described, all of whom are substantive employees of i5 Health. Data will only be accessed at the named processing location as set out in this application. The additional SUS PbR Data being provided will be linked to SUS PbR data already held, across the datasets (e.g. SUS PbR Episode data with SUS PbR A&E data); from National Level to GP Practice Level. There will be no requirement nor attempt to re-identify individuals within the datasets. National data is required as i5 Health provide reports from local through Regional to National levels. Multiple years of data are required in order to produce time-series and predictive modelling, historic data is retained to enable this. The data cannot be minimised by applying filters to specific conditions of relevance as the full data is needed in order to produce the outputs as outlined within the application. Data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. For example, a report containing aggregate data for cohorts from localities to large geographical areas will be produced for NHS England. The inclusion of Voluntary Sector Organisations (VSO) as recipients in the ‘commercial’ purpose context outlined below in no way changes the processing activities set out in this paragraph, but will be limited to aggregate data.	The outputs will be aggregated analysis with small numbers suppressed for inclusion within economic evaluation and Clinical Commissioning Group (CCG) strategy. All outputs are solely provided to the NHS customers and no service/product/data will be supplied to any commercial organisation by i5 Health except in so far as is permitted for Voluntary Sector Organisations (VSO) for the purpose outlined in the ‘commercial’ purpose section below. The data provided will be used solely for the purposes identified above. The outputs i5 Health Limited have provided over the last 24 months are (according to each purpose); Purpose #1) Non-Medical Prescribing • Creation of first national report for Health Education England on the economic value of Non-Medical Prescribing called on three categories of HSCIC information (latest HES data in respect of long term health conditions (LTC); Nurses currently in the workforce, and Nurses using FP10 Prescription forms). Purpose 2) Consultancy Service • A number of commissioning support reports are made for CCGs within the footprint of Business Intelligence partners, Arden & GEM CSU; NHS England has missioned i5 Health to provide commissioning reports for 32 CCGs and 5 Sustainability and Transformation Plans (STP) in London as well as analysis of the effect on London over the next five years of the introduction of Digital technology;. Purpose #2.1) End of Life • As part of NHS England’s Electronic Palliative Care Co-ordination System (EPaCCS) programme, i5 Health Limited carried out an evaluation of data in respect of End of Life and its related costs. This is a continuing project – the outcomes being dependent on changes in trends of data. The studies have been carried out at the specific requests of the various CCGs within England and Wales. Purpose #2.2) Case Finding • In many parts of the country, a number of LTC patients are sub-optimally treated because they fail to get on to the relevant registers at GP practices; additionally, there are many patients that have conditions which, if identified early enough, could receive treatment that reduces the risk of them progressing to a full LTC. i5 Health has developed algorithms that identify, at surgery level, the numbers of patients that fall into both these categories. NHS England requires i5 Health to carry out a study in respect of the GP practices in Southport and Formby. This NHS England initiative is continuing. Purpose #2.3) Urgent Care • Halton CCG needed to map out the Urgent Care pressure across all the supporting Hospitals. Linked with this exercise, they commissioned i5 Health, using the Commissioning Opportunity module (COP), to investigate the patient urgent care journey. (i5 Health Limited are now discussing applying the same skills for the benefit of other North West CCGs including South Sefton CCG and Southport and Formby CCG). Purpose #2.3) Readmissions • Halton CCG asked for the assistance of i5 Health in analysing significant Readmissions issues. The analysis, based on the COP algorithms, got right to the heart of the problem and identified a significant number of patients that, under normal circumstances, should not have undergone readmission. Purpose #2.3) Outpatient Procedures • On behalf of Halton CCG, More recently, i5 Health performed analysis of into what has been happening in respect of Outpatient episodes and then developed some solutions to excessive use in Cardiology, Mouth/Head/Neck & Ears, Orthopaedic Non-Trauma and Urology. Purpose #2.3) ACS – Respiratory and Ear, Nose, Throat (ENT) • i5 Health established what might, currently, be the best opportunity for Halton CCG to reduce acute care activity and cost - dealing with Respiratory and Ear Nose Throat conditions. Besides analysing historic and current situation, i5 Health examined six case studies to establish, using a Population Health Management approach, what might be optimum strategies for to pursue (the product of this work is now being leveraged into the Case Finding activity).	The Non-Medical Prescribing (NMP) has been in existence for 26 years. Over time there are good reasons to believe that the returns from it, across the board, have been very positive: from cost effectiveness, through staff development to patient satisfaction. Not least of all, the clinicians Audit, in growing use since 2009, has elicited important data supportive of that contention. However, greater evidence of the performance and effect of NMP is necessary. i5 Health is therefore being asked to review existing HES and studies to draw out relevant information, propose new methodology, refine existing audits and promote new ones to provide a comprehensive analysis of NMP to assist in decisions on whether and to what extent NMP should be adopted more widely in England. Voluntary Sector Organisations (VSO) cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients. The benefits to the UK Health and Social Care system are better strategic planning and commissioning decisions, and subsequently improved care for patients due to better planning and strategy. The financial benefit for the healthcare system varies from case to case. By way of example, analysis by i5 Health for the Sussex health economy two years ago resulted in a 10% reduction in Non-Elective admissions (NEL) thus saving hundreds of thousands of pounds annually. Benefits achieved in the last 10 months; Purpose #1) Non-Medical Prescribing • With increasing pressure on the availability of doctors in both primary and secondary care, there is a growing case for greater use of NMP i.e. prescribing by a non-doctor (e.g. nurse, pharmacist, etc..). That case is reinforced by the cost/benefit identified and the better levels of care demonstrated. Purpose #2.1) End of Life • The HES based studies so far are showing a disturbing picture particularly, though not exclusively, in respect of the frail and elderly in their last year of life. The concerns are around the high levels of admissions to hospitals and the distress to patients this causes. Further work needs to be done for some CCGs on identifying, with business cases, the alternative strategies that can answer the above concerns. Purpose #2.1) Case Finding • Identification of patients that have (or risk having) an Long Term Condition (LTC) but do not appear on the GPs risk register can lead to better management of their health requirements. Purpose #2.3) Urgent Care • Commissioning Opportunity (COP) is all about matching patient groups against successful healthcare initiatives and forecasting the effect of local implementation on patient care and budgets. Purpose #2.3) Readmissions • Issues relating to specific surgeons were highlighted. i5 Health went beyond problem identification and evaluation and made detailed and well considered recommendations – not just for the CCG but also its providers and colleagues across primary and community care. Purpose #2.3) Outpatients • The i5 Health solutions included alternatives for 6,000 procedures currently costing over £1.5m. The solutions are being implemented and will result in significant cost savings. Purpose #2.3) ACS - Respiratory and ENT • One of the many positive outcomes of the exercise has been the identification of over 200 patients clinically diagnosed with COPD in secondary care that are not on the GPs risk register and which are likely to be unmanaged.
I5 HEALTH	I5 HEALTH	Bespoke Monthly Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	i5 Health Limited (i5 Health) requires health data for the following purposes: Purpose #1) i5 Health Limited (i5 Health) evaluates, on behalf of the Health Education Board of NHS England, the economic impact of Non-Medical Prescribing (NMP) - the prescribing of drugs by health practitioners other than doctors. i5 Health analyses the relevant activity data in order to identify utilisation of NMP practitioners in various healthcare settings. In doing so, they can measure the impact NMP has or, if introduced more widely, will have on different health economies. (Academic Paper ID: WNC 48 'Nurse Prescribing' - WorldwideNursingConference,Singapore2014;abstract http://www.citeulike.org/user/gstf/article/13247895 ) First full first report (http://www.i5health.com/NMP/NMPEconomicEvaluation.pdf ) to be updated when i5 Health receives 2016/17 data. Purpose #2) i5 Health provides consultancy services to support to Clinical Commissioning Groups (CCG), CSUs, Sustainability and Transformation Plans (STP), Acutes, NHS England and Local Authorities (LA) in their decision making for commissioning purposes. The specific purposes are:- Purpose #2.1) To identify realistic NHS Quality, Innovation, Productivity and Prevention (QIPP) QIPP initiatives for specific CCGs, Commissioning Support Units (CSU) and Providers in order to spot trends and to perform benchmarking that support commissioners in particular with their operational, strategic planning and co-commissioning. Current work includes with NHS England to identify suitable initiatives for Specialist Services like Cardiology and Cardiac Surgery. It also includes provision of patient counts for Long Term Conditions (LTC) to GPs to enable them to evaluate the quality of their Quality Outcome Framework (QOF) registers and devise appropriate actions (with small numbers suppressed). Purpose #2.2) i5 Health advises Voluntary Sector Organisations (VSOs) that have charitable status and exist to complement the work of the NHS in improving patient care. Such VSOs include Age UK and Asthma UK. Only voluntary organisations that are commissioned by the NHS will be clients of this service. Purpose #2.3) To measure standards of care and identify gaps in provision to inform commissioning strategy. A number of CCGs including NHS Halton CCG, C4G CCG, Brent CCG, Ashford CCG, have been working with i5 Health in this respect to develop their strategies. Where NHS Digital has already given formal approval for i5 Health to analyse data (IG Ref DSCON066/Halton CCG), the outcome was described by the Director of Transformation as giving; "…..Halton CCG a unique glance into what financial results could be made through our partnership approach. Unlike any other piece of consultancy, i5 and COP shone an economic light on what schemes are working well and what areas i5 Health could prioritise our energy on." i5 Health requires SUS PBR spells & episode at patient level, including procedure and diagnosis codes, in order to evaluate the applicability of a particular QIPP initiative for a group of patients. Data on PBR spells and episodes is essential in i5 Health establishing the nature and size of specific patient cohorts in a given acute provider setting. Such identification allows i5 Health to calculate accurately the effect of any proposed, specific initiative including the financial impact of that change (e.g. provision of certain alternatives in the primary care sector to hospital treatment). VSOs already cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients.	NHS Digital will provide i5Health with record level pseudo/anonymised SUS PbR data via the Secure Electronic File Transfer (SEFT) system. A Database Analyst (DBA) from i5Health will load the record level data into a database. The database will be managed locally and secured by the DBA with user access control. i5 Health will be using SQL Server 2008 on a bit locker encrypted partition. Record-level data will only be accessed by individuals within the Analytics Team, who have the authorisation from the Operations Director (who is also Caldicott Guardian), to access the data for the purpose (s) described, all of whom are substantive employees of i5 Health. Data will only be accessed at the named processing location as set out in this application. The additional SUS PbR Data being provided will be linked to SUS PbR data already held, across the datasets (e.g. SUS PbR Episode data with SUS PbR A&E data); from National Level to GP Practice Level. There will be no requirement nor attempt to re-identify individuals within the datasets. National data is required as i5 Health provide reports from local through Regional to National levels. Multiple years of data are required in order to produce time-series and predictive modelling, historic data is retained to enable this. The data cannot be minimised by applying filters to specific conditions of relevance as the full data is needed in order to produce the outputs as outlined within the application. Data will not be made available to any third parties except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. For example, a report containing aggregate data for cohorts from localities to large geographical areas will be produced for NHS England. The inclusion of Voluntary Sector Organisations (VSO) as recipients in the ‘commercial’ purpose context outlined below in no way changes the processing activities set out in this paragraph, but will be limited to aggregate data.	The outputs will be aggregated analysis with small numbers suppressed for inclusion within economic evaluation and Clinical Commissioning Group (CCG) strategy. All outputs are solely provided to the NHS customers and no service/product/data will be supplied to any commercial organisation by i5 Health except in so far as is permitted for Voluntary Sector Organisations (VSO) for the purpose outlined in the ‘commercial’ purpose section below. The data provided will be used solely for the purposes identified above. The outputs i5 Health Limited have provided over the last 24 months are (according to each purpose); Purpose #1) Non-Medical Prescribing • Creation of first national report for Health Education England on the economic value of Non-Medical Prescribing called on three categories of HSCIC information (latest HES data in respect of long term health conditions (LTC); Nurses currently in the workforce, and Nurses using FP10 Prescription forms). Purpose 2) Consultancy Service • A number of commissioning support reports are made for CCGs within the footprint of Business Intelligence partners, Arden & GEM CSU; NHS England has missioned i5 Health to provide commissioning reports for 32 CCGs and 5 Sustainability and Transformation Plans (STP) in London as well as analysis of the effect on London over the next five years of the introduction of Digital technology;. Purpose #2.1) End of Life • As part of NHS England’s Electronic Palliative Care Co-ordination System (EPaCCS) programme, i5 Health Limited carried out an evaluation of data in respect of End of Life and its related costs. This is a continuing project – the outcomes being dependent on changes in trends of data. The studies have been carried out at the specific requests of the various CCGs within England and Wales. Purpose #2.2) Case Finding • In many parts of the country, a number of LTC patients are sub-optimally treated because they fail to get on to the relevant registers at GP practices; additionally, there are many patients that have conditions which, if identified early enough, could receive treatment that reduces the risk of them progressing to a full LTC. i5 Health has developed algorithms that identify, at surgery level, the numbers of patients that fall into both these categories. NHS England requires i5 Health to carry out a study in respect of the GP practices in Southport and Formby. This NHS England initiative is continuing. Purpose #2.3) Urgent Care • Halton CCG needed to map out the Urgent Care pressure across all the supporting Hospitals. Linked with this exercise, they commissioned i5 Health, using the Commissioning Opportunity module (COP), to investigate the patient urgent care journey. (i5 Health Limited are now discussing applying the same skills for the benefit of other North West CCGs including South Sefton CCG and Southport and Formby CCG). Purpose #2.3) Readmissions • Halton CCG asked for the assistance of i5 Health in analysing significant Readmissions issues. The analysis, based on the COP algorithms, got right to the heart of the problem and identified a significant number of patients that, under normal circumstances, should not have undergone readmission. Purpose #2.3) Outpatient Procedures • On behalf of Halton CCG, More recently, i5 Health performed analysis of into what has been happening in respect of Outpatient episodes and then developed some solutions to excessive use in Cardiology, Mouth/Head/Neck & Ears, Orthopaedic Non-Trauma and Urology. Purpose #2.3) ACS – Respiratory and Ear, Nose, Throat (ENT) • i5 Health established what might, currently, be the best opportunity for Halton CCG to reduce acute care activity and cost - dealing with Respiratory and Ear Nose Throat conditions. Besides analysing historic and current situation, i5 Health examined six case studies to establish, using a Population Health Management approach, what might be optimum strategies for to pursue (the product of this work is now being leveraged into the Case Finding activity).	The Non-Medical Prescribing (NMP) has been in existence for 26 years. Over time there are good reasons to believe that the returns from it, across the board, have been very positive: from cost effectiveness, through staff development to patient satisfaction. Not least of all, the clinicians Audit, in growing use since 2009, has elicited important data supportive of that contention. However, greater evidence of the performance and effect of NMP is necessary. i5 Health is therefore being asked to review existing HES and studies to draw out relevant information, propose new methodology, refine existing audits and promote new ones to provide a comprehensive analysis of NMP to assist in decisions on whether and to what extent NMP should be adopted more widely in England. Voluntary Sector Organisations (VSO) cooperate with i5 Health to improve the extent and quality of the information that i5 Health relies on to support, with data processing, clinical commissioning within the NHS. The VSOs have occasion to ask for i5 Health reports, based on data analysis that can improve their own specific charitable works for NHS patients. The benefits to the UK Health and Social Care system are better strategic planning and commissioning decisions, and subsequently improved care for patients due to better planning and strategy. The financial benefit for the healthcare system varies from case to case. By way of example, analysis by i5 Health for the Sussex health economy two years ago resulted in a 10% reduction in Non-Elective admissions (NEL) thus saving hundreds of thousands of pounds annually. Benefits achieved in the last 10 months; Purpose #1) Non-Medical Prescribing • With increasing pressure on the availability of doctors in both primary and secondary care, there is a growing case for greater use of NMP i.e. prescribing by a non-doctor (e.g. nurse, pharmacist, etc..). That case is reinforced by the cost/benefit identified and the better levels of care demonstrated. Purpose #2.1) End of Life • The HES based studies so far are showing a disturbing picture particularly, though not exclusively, in respect of the frail and elderly in their last year of life. The concerns are around the high levels of admissions to hospitals and the distress to patients this causes. Further work needs to be done for some CCGs on identifying, with business cases, the alternative strategies that can answer the above concerns. Purpose #2.1) Case Finding • Identification of patients that have (or risk having) an Long Term Condition (LTC) but do not appear on the GPs risk register can lead to better management of their health requirements. Purpose #2.3) Urgent Care • Commissioning Opportunity (COP) is all about matching patient groups against successful healthcare initiatives and forecasting the effect of local implementation on patient care and budgets. Purpose #2.3) Readmissions • Issues relating to specific surgeons were highlighted. i5 Health went beyond problem identification and evaluation and made detailed and well considered recommendations – not just for the CCG but also its providers and colleagues across primary and community care. Purpose #2.3) Outpatients • The i5 Health solutions included alternatives for 6,000 procedures currently costing over £1.5m. The solutions are being implemented and will result in significant cost savings. Purpose #2.3) ACS - Respiratory and ENT • One of the many positive outcomes of the exercise has been the identification of over 200 patients clinically diagnosed with COPD in secondary care that are not on the GPs risk register and which are likely to be unmanaged.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Imperial College London Doctor Foster Unit (ICL DFU) uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. ICL DFU works in collaboration with Dr Foster Limited (DFI) to provide a management information function in the form of analysis for healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. Both ICL DFU and DFI require the full HES datasets to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with lesser fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to Dr Foster Intelligence Limited (DFI) customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers and ALL NHS Trusts. DFI has no access to the patient re-identification service. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. For this purpose, ICL DFU have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Limited Analysis Toolkit. This service allows supply of Provider trusts’ NHS Number and LOPATID using Dr Foster Limited Analysis Toolkit without passing these fields on to DFI. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from DFI. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to DFI. The unit works in collaboration with DFI to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, DFI responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the DFI Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. DFI also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by DFI or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. DFI is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Imperial College London Doctor Foster Unit (ICL DFU) uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. ICL DFU works in collaboration with Dr Foster Limited (DFI) to provide a management information function in the form of analysis for healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. Both ICL DFU and DFI require the full HES datasets to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with lesser fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to Dr Foster Intelligence Limited (DFI) customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers and ALL NHS Trusts. DFI has no access to the patient re-identification service. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. For this purpose, ICL DFU have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Limited Analysis Toolkit. This service allows supply of Provider trusts’ NHS Number and LOPATID using Dr Foster Limited Analysis Toolkit without passing these fields on to DFI. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from DFI. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to DFI. The unit works in collaboration with DFI to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, DFI responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the DFI Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. DFI also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by DFI or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. DFI is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	Imperial College London Doctor Foster Unit (ICL DFU) uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. ICL DFU works in collaboration with Dr Foster Limited (DFI) to provide a management information function in the form of analysis for healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. Both ICL DFU and DFI require the full HES datasets to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with lesser fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to Dr Foster Intelligence Limited (DFI) customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers and ALL NHS Trusts. DFI has no access to the patient re-identification service. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. For this purpose, ICL DFU have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Limited Analysis Toolkit. This service allows supply of Provider trusts’ NHS Number and LOPATID using Dr Foster Limited Analysis Toolkit without passing these fields on to DFI. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from DFI. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to DFI. The unit works in collaboration with DFI to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, DFI responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the DFI Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. DFI also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by DFI or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. DFI is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Imperial College London Doctor Foster Unit (ICL DFU) uses HES data to identify measures of quality and safety in healthcare. Their research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. ICL DFU works in collaboration with Dr Foster Limited (DFI) to provide a management information function in the form of analysis for healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. Both ICL DFU and DFI require the full HES datasets to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard casemix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with lesser fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Patient identifiers The Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002 (s251) support letter confirms the final approval to receive confidential patient information for ICL DFU research database and identifiers to provide re-identification service to Dr Foster Intelligence Limited (DFI) customers and ALL NHS trusts. Identifiable data processed under CAG [15/CAG/0005] will be retained for a maximum of three years after which it should be destroyed or irreversibly pseudonymised on a rolling basis. The purpose of holding the patient identifiers is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. ICL DFU does this by providing a re-identification service to acute NHS providers who are Dr Foster Limited’s customers and ALL NHS Trusts. DFI has no access to the patient re-identification service. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. For this purpose, ICL DFU have developed a re-identification service whereby authorised individuals within NHS Provider Trusts are able to identify their own patients indicated in the Dr Foster Limited Analysis Toolkit. This service allows supply of Provider trusts’ NHS Number and LOPATID using Dr Foster Limited Analysis Toolkit without passing these fields on to DFI. The re-identification service is maintained by ICL DFU. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from DFI. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to DFI. The unit works in collaboration with DFI to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, DFI responds to customer requests for analyses of HSCIC data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the DFI Head of Information Governance or SIRO will provide guidance and if required contact HSCIC. DFI also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by DFI or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. DFI is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Objective for processing: The Small Area Health Statistics Unit (SAHSU) is a long-standing and internationally-recognised centre of excellence assessing the risk of exposure to environmental pollutants to the health of the population, with an emphasis on the use and interpretation of routine health statistics at small-area level. SAHSU was established in 1987 as a recommendation of the Black enquiry into the incidence of leukaemia and lymphoma in children and young adults near the Windscale/Sellafield nuclear power plant. SAHSU has a particular role nationally in carrying out environmental health surveillance of the population in relation to environmental contaminants and point sources of industrial pollution, based on routinely collected health data. This is a highly specialised area of work requiring excellence in computing, database management, geographical information systems (GIS), statistics, environmental exposure assessment and epidemiology. The set of skills and expertise that has been established and built up in SAHSU is a unique resource both nationally and worldwide. SAHSU has a programme of work established which is defined by the following terms of reference :- 1) To develop and maintain databases of health data, environmental exposures as required to meet specific need, and social confounding factors at the small area level; 2) To carry out substantive research studies on environment and health issues including studies of the relationship between socio-economic factors and health, in collaboration with other scientific groups as necessary; 3) In collaboration with other scientific groups, to build up reliable background information on the distribution of environmental exposure, socio-economic data and disease amongst small areas; 4) To develop methodology for analysing and interpreting health outcomes related to small areas; 5) To act as a centre of expertise, disseminating information on developments in spatial epidemiological methods to national and regional groups; 6) To respond rapidly, with expert advice, to ad hoc queries from the core funding bodies (DH and PHE) about unusual clusters of disease, particularly in the neighbourhood of industrial installations To deliver against that Terms of Reference, SAHSU will utilise the data to meet the following purposes: Purpose 1 – maintenance of the SAHSU health research database At the core of the overall programme of work is the maintenance of the SAHSU research database, which combines multiple datasets and has both ethics approval and s251 approval in place Purpose 2 – to carry out a programme of research projects and studies. The research programme includes both methods development and investigation of priority questions in environment and health. A key aim is to improve the science base underlying translation of knowledge on the effects of the environment on health into policy. SAHSU conducts national research studies on environmental factors that may affect health ranging from exposures to electromagnetic fields (such as from electricity power lines) to traffic-related air pollution and noise, using nationally collected patient data including mortality, hospital admissions, cancer registrations and births data. Additional small area analyses are conducted that help support the general remit of the unit e.g. investigating differential hospital admission rates in ethnically diverse small areas. SAHSU provides national expertise in cluster and small area statistical methods and has close links with Public Health England including input into their environmental public health tracking programme and surveillance activities. Approval of individual projects All SAHSU studies are controlled via the PHE-SAHSU Liaison Committee. New study concepts must initially be approved by either the Director or Assistant director of SAHSU prior to the outline study proposal being created. Consideration is given to whether the study is adequately covered by SAHSU’s existing ethical approval and if not, separate ethical approval must be sought. Once ethical approval is confirmed the outline study proposal is reviewed by the SAHSU-PHE liaison committee who, once approved then take the study for formal minuted approval from the appropriate PHE programme board (attended by a member of the Department of Health). Projects are only approved where they are within the constraints of the SAHSU programme terms of reference. Purpose 3 – to provide rapid ad hoc support to PHE and DH about unusual clusters of disease, particularly in the neighbourhood of industrial installations The rapid response function is in our contract with Public Health England (i.e. mandated and funded by PHE). Such work is carried out on the instruction of DH or PHE, and is approved by the Director of SAHSU. By its nature, such requirements cannot be detailed in advance, but the only outputs would be aggregate with small numbers suppressed, and provided to DH and PHE. The Rapid Inquiry Facility (RIF) is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. A newer version of the Rapid Inquiry Facility (RIF) software is currently being developed. This version will allow us to test on various scenarios to help detect clusters or increased rates of disease near industrial installations. As a result of the RIF being redeveloped, we have been doing less on this function (due to less capacity/resources available) but we are expecting to be doing more over the next 1-2 years. As an example of the most recent request we have just responded to a request from the Committee on the Medical Aspects of Radiation in the Environment (COMARE) to use the RIF to provide ongoing surveillance of cancer around nuclear power stations using cancer registrations. SAHSU’s Welcome Trust seed award on “Public health surveillance of chronic diseases: suitability of spatio-temporal methods” starting June 2017 for two years will be testing capacity of HES and HES monthly to look at clusters and appropriate statistical methods to use which may vary by disease type. SAHSU have a single s251 support in place to cover the above. Any project which may have additional requirements (for example to require the linkage of an additional dataset beyond that previously agreed) must seek an amendment to the existing s251. It would also be outside the scope of this application, and therefore an amendment would be put before DAAG for consideration.	Processing activities: Processing is consistent across all three purposes, given that they all require the use of the same research database, In summary :- - identifiable data are encrypted and held in a secure area of the database on the SAHSU private network. Access to the identifiable data is limited to a small database team within SAHSU. - The identifiable data is held on a separate encrypted file system with access limited to the database team only. The pseudonymised output (CSV) file is then loaded into Oracle for use by researchers. - Separation is maintained between the database team, who handle data encryption and see identifiable data, and researchers, who only have access to pseudonymised data. NHS numbers, addresses and postcodes are encrypted and replaced by pseudonyms and held in confidential tables to which only the database team has access. Record separation occurs during data loading; the original files are stored on a separate encrypted file system on a separate server. Identifiers are not held on the same server as the clinical data. Further processing standardises names, data types, correct dates, links in geography via the postcode, performs encryption and pseudonymisation and carries out dataset specific bespoke processing (e.g. the detection of potential duplicates). At this point the data is only accessible by the database team, and is fully protected by encryption and pseudonymisation. The next phase creates production tables and sets them up for use by the researchers, granting appropriate permissions and setting up auditing. There then follows an extensive set of quality control checks; these are documented in the database. Finally, documentation is automatically generated. When the process is complete the load tables are dumped to the encrypted file system for reference and then removed. It is therefore practicable to reload SAHSU data at intervals to enhance security and to add processing improvements to pre-existing data (e.g. improved data processing, enhanced security, improved pseudonymisation). It is normal SAHSU practice to reload datasets each time a fresh year is received to ensure the latest processing is uniformly applied to all data. SAHSU operates a hierarchy of data access permission based on user role: 1. General level access to aggregated health data, such as that publicly available from data providers websites e.g. district level mortality counts; 2. SAHSU researcher with access to small area data that is not pseudonymised and non-sensitive; 3. SAHSU researcher level access to pseudonymised sensitive data where required for specific projects; 4. Database team access to identifiable information supplied by data providers e.g. to pseudonymise the data. All access to confidential data is password protected. Data may only be extracted by the database team, or by using a “self-select” tool called Rapid Inquiry Facility (RIF). Note that this tool allows the user to select the nature of data to be extracted, but only at in an aggregated form. The RIF is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. This facility was initially designed as a tool for SAHSU staff to analyse routinely collected health data in relation to environmental exposures in the European Health and Environment Information System (EUROHEIS) project and has also been used by SAHSU to provide aggregated information as part of the US Centers for Disease Control (CDC) environmental public health tracking. In all cases data is extracted using the Rapid Enquiry Facility (RIF) or by the database team, and all extracts are supervised by the database manager. All data extracts are logged and cross checked by the database manager prior to extraction. The RIF will also permit data extraction and will also enforce identical checks. The checks carried out prior to data extraction are: • Projects is approved by the SAHSU liaison committee; • User is under contract to Imperial; • If required, access to event data (date of birth and/or death) has been justified; • SAHSU confidentiality form has been signed ; • The user has been information governance inducted and trained. In addition to the data provided by the HSCIC, SAHSU hold the following record level identifiable datasets: • ONS Births and Still births • ONS Cancer Incidence • Welsh Cancer Intelligence and Surveillance Unit • ONS Mortality • National Congenital Anomaly Register (NCAR from ONS) • Local Congenital Anomaly registries affiliated with BINOCAR • Terminations grounds “E” • NN4B • NCCHD (National Community Child Health Database) Linkage of data between datasets is only permitted with: • Approval via a substantial amendment to SAHSU ethics approval • Approval via a substantial amendment from HRA CAG • Explicit written permission from the data providers concerned. To date, amendments to the s.251 support have been sought and granted for the following three projects requiring specific data linkage: 1. Traffic pollution and health in London; 2. Incinerators; 3. Small area variation in coronary heart disease incidence, mortality and survival and their risk factors and determinants in England. Any projects requiring linkage beyond that covered by this application would also be subject to an amendment for DAAG’s consideration. It would also require support from ethics, and be covered by an amendment to the existing s251. Data minimisation As part of this application, the data required has been rationalised and HES data currently held by SAHSU no longer covered by this agreement will be securely destroyed. The remaining years are required by a number of studies, but the totality of years of data is also required - e.g. in a study relating to health effects in relation to environmental exposures from major airports. Whilst the initial study is complete, the data is required to be retained in order to respond to any queries relating to the research (a common requirement for published research). National data is also required in order to provide the ad hoc rapid response service to Public Health England. Given that PHE coverage is across England, and the requirements cannot be predicted in advance, national data is required given that response times do not permit time to request, receive and process individual extracts of data.	Specific outputs expected, including target date: There are three main types of output from this application :- 1. The maintenance of the research database, and associated record level pseudonymised extracts for use by individual researchers within SAHSU. Such extracts are delivered either through the RIF or the database team. This database will have the ability to provide data for the specific projects which have been approved as set out above. Note that all extracts are subject to the constraints within this agreement, eg: that data will only be held and processed at the processing / storage address. Such work is on-going through the lifetime of this agreement. 2. Individual project research outputs. All such outputs would be aggregate data, and be used within journal papers, research reports and results, presentations at conferences. Such research outputs would be placed into the public domain. Examples of previous outputs and projects are given below. 3. Individual analyses for Public Health England (PHE). Again, all such outputs would be in aggregated form, but would be provided directly to PHE. Examples of outputs produced to date -Traffic pollution and health in London study Recent papers published include: • Halonen, JI et al. Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. European Heart Journal 2015. • Gulliver, J et al. Development of an open-source road traffic noise model for exposure assessment. Environmental Modelling & Software. 2015. • Halonen JI et al. Is long -term exposure to traffic pollution associated with mortality? A spatial analysis in London. Environmental Pollution. 2015. Future outputs from the study will be disseminated via peer reviewed publication and academic conferences presentations. The outputs of the project will be published in peer-reviewed journals during the course of the study and after completion, which was expected by 2016 but will now be in 2017. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) There have been three papers published to date arising from work on this study: • Font A et al. Using Metal Ratios to Detect Emissions from Municipal Waste Incinerators in Ambient Air Pollution Data. Atmospheric Environment. 2015 • Ashworth DC, et al. Comparative assessment of particulate air pollution exposure from municipal solid waste incinerator emissions. Journal of Environmental and Public Health. 2013 • Ghosh R, et al. Ghosh RE, Ashworth DC, Hansell AL, Garwood K, Elliott P, Toledano MB. Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology. Arch Dis Child Fetal Neonatal Ed. 2016 Further papers are expected in 2017. Study information is provided on the SAHSU website for the public. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study have been published in the peer-reviewed BMJ (http://www.bmj.com/content/347/bmj.f5432 ). All outputs from individual projects will be anonymised (data will only be shared where aggregated with small numbers suppressed in line with the HES Analysis Guide). To confirm, no record level data is provided to any third party organisation and no commercial use is permitted.	Expected measurable benefits to health and/or social care including target date: The focus of the work under this application is to enable key public health issues associated with environmental factors at a small area. This would by definition therefore not look at care of individuals, but would related to informing public health policy and considering population health risks. The key points in demonstrating the benefit to health and social care therefore are :- - The core SAHSU programme of work is funded by Public Health England, and this includes the service to Public Health England to assist PHE in fulfilling their duties - Individual projects must meet be aligned with the Terms of Reference of the SAHSU programme, which includes addressing environmental and health issues. As part of this application, SAHSU will be amending these conditions to include an explicit requirement to reference that projects must be for the promotion of health - All projects are approved by the PHE Programme Board, which includes membership from DH. In order to assist with standardising approaches across application processes, SAHSU have offered membership of the approving group to the Data Access Request Service. Whilst individual detailed project related benefits cannot be stated at this time, three examples are given below of how outputs have and will be used to inform health policy. -Traffic pollution and health in London study The results of this study allow for a better understanding of the health problems caused by air pollution and noise from traffic in London. Findings to date have received a high media-profile and have the potential to influence air pollution policy and regulatory practices in London and the UK. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) The study was commissioned to extend the evidence base and to provide further information to the public about any potential reproductive and infant health risks from MWIs and to extend the evidence base with respect to exposures and any potential reproductive and infant health risks from MWIs. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study allow for better understanding of the health problems caused by noise from aircraft in London. The findings had a high media-profile and are directly relevant to the Davies commission and decisions on whether to build a third run-way at Heathrow. Results of this study have been reported widely in local, national and international media and been raised as questions at Prime-ministers question. The results of SAHSU studies are placed in the public domain via peer-reviewed publications and are used to inform the behaviour of health care providers and to inform national public health policy. All studies equally feedback their results into the relevant policy leads within PHE.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Accident and Emergency	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Objective for processing: The Small Area Health Statistics Unit (SAHSU) is a long-standing and internationally-recognised centre of excellence assessing the risk of exposure to environmental pollutants to the health of the population, with an emphasis on the use and interpretation of routine health statistics at small-area level. SAHSU was established in 1987 as a recommendation of the Black enquiry into the incidence of leukaemia and lymphoma in children and young adults near the Windscale/Sellafield nuclear power plant. SAHSU has a particular role nationally in carrying out environmental health surveillance of the population in relation to environmental contaminants and point sources of industrial pollution, based on routinely collected health data. This is a highly specialised area of work requiring excellence in computing, database management, geographical information systems (GIS), statistics, environmental exposure assessment and epidemiology. The set of skills and expertise that has been established and built up in SAHSU is a unique resource both nationally and worldwide. SAHSU has a programme of work established which is defined by the following terms of reference :- 1) To develop and maintain databases of health data, environmental exposures as required to meet specific need, and social confounding factors at the small area level; 2) To carry out substantive research studies on environment and health issues including studies of the relationship between socio-economic factors and health, in collaboration with other scientific groups as necessary; 3) In collaboration with other scientific groups, to build up reliable background information on the distribution of environmental exposure, socio-economic data and disease amongst small areas; 4) To develop methodology for analysing and interpreting health outcomes related to small areas; 5) To act as a centre of expertise, disseminating information on developments in spatial epidemiological methods to national and regional groups; 6) To respond rapidly, with expert advice, to ad hoc queries from the core funding bodies (DH and PHE) about unusual clusters of disease, particularly in the neighbourhood of industrial installations To deliver against that Terms of Reference, SAHSU will utilise the data to meet the following purposes: Purpose 1 – maintenance of the SAHSU health research database At the core of the overall programme of work is the maintenance of the SAHSU research database, which combines multiple datasets and has both ethics approval and s251 approval in place Purpose 2 – to carry out a programme of research projects and studies. The research programme includes both methods development and investigation of priority questions in environment and health. A key aim is to improve the science base underlying translation of knowledge on the effects of the environment on health into policy. SAHSU conducts national research studies on environmental factors that may affect health ranging from exposures to electromagnetic fields (such as from electricity power lines) to traffic-related air pollution and noise, using nationally collected patient data including mortality, hospital admissions, cancer registrations and births data. Additional small area analyses are conducted that help support the general remit of the unit e.g. investigating differential hospital admission rates in ethnically diverse small areas. SAHSU provides national expertise in cluster and small area statistical methods and has close links with Public Health England including input into their environmental public health tracking programme and surveillance activities. Approval of individual projects All SAHSU studies are controlled via the PHE-SAHSU Liaison Committee. New study concepts must initially be approved by either the Director or Assistant director of SAHSU prior to the outline study proposal being created. Consideration is given to whether the study is adequately covered by SAHSU’s existing ethical approval and if not, separate ethical approval must be sought. Once ethical approval is confirmed the outline study proposal is reviewed by the SAHSU-PHE liaison committee who, once approved then take the study for formal minuted approval from the appropriate PHE programme board (attended by a member of the Department of Health). Projects are only approved where they are within the constraints of the SAHSU programme terms of reference. Purpose 3 – to provide rapid ad hoc support to PHE and DH about unusual clusters of disease, particularly in the neighbourhood of industrial installations The rapid response function is in our contract with Public Health England (i.e. mandated and funded by PHE). Such work is carried out on the instruction of DH or PHE, and is approved by the Director of SAHSU. By its nature, such requirements cannot be detailed in advance, but the only outputs would be aggregate with small numbers suppressed, and provided to DH and PHE. The Rapid Inquiry Facility (RIF) is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. A newer version of the Rapid Inquiry Facility (RIF) software is currently being developed. This version will allow us to test on various scenarios to help detect clusters or increased rates of disease near industrial installations. As a result of the RIF being redeveloped, we have been doing less on this function (due to less capacity/resources available) but we are expecting to be doing more over the next 1-2 years. As an example of the most recent request we have just responded to a request from the Committee on the Medical Aspects of Radiation in the Environment (COMARE) to use the RIF to provide ongoing surveillance of cancer around nuclear power stations using cancer registrations. SAHSU’s Welcome Trust seed award on “Public health surveillance of chronic diseases: suitability of spatio-temporal methods” starting June 2017 for two years will be testing capacity of HES and HES monthly to look at clusters and appropriate statistical methods to use which may vary by disease type. SAHSU have a single s251 support in place to cover the above. Any project which may have additional requirements (for example to require the linkage of an additional dataset beyond that previously agreed) must seek an amendment to the existing s251. It would also be outside the scope of this application, and therefore an amendment would be put before DAAG for consideration.	Processing activities: Processing is consistent across all three purposes, given that they all require the use of the same research database, In summary :- - identifiable data are encrypted and held in a secure area of the database on the SAHSU private network. Access to the identifiable data is limited to a small database team within SAHSU. - The identifiable data is held on a separate encrypted file system with access limited to the database team only. The pseudonymised output (CSV) file is then loaded into Oracle for use by researchers. - Separation is maintained between the database team, who handle data encryption and see identifiable data, and researchers, who only have access to pseudonymised data. NHS numbers, addresses and postcodes are encrypted and replaced by pseudonyms and held in confidential tables to which only the database team has access. Record separation occurs during data loading; the original files are stored on a separate encrypted file system on a separate server. Identifiers are not held on the same server as the clinical data. Further processing standardises names, data types, correct dates, links in geography via the postcode, performs encryption and pseudonymisation and carries out dataset specific bespoke processing (e.g. the detection of potential duplicates). At this point the data is only accessible by the database team, and is fully protected by encryption and pseudonymisation. The next phase creates production tables and sets them up for use by the researchers, granting appropriate permissions and setting up auditing. There then follows an extensive set of quality control checks; these are documented in the database. Finally, documentation is automatically generated. When the process is complete the load tables are dumped to the encrypted file system for reference and then removed. It is therefore practicable to reload SAHSU data at intervals to enhance security and to add processing improvements to pre-existing data (e.g. improved data processing, enhanced security, improved pseudonymisation). It is normal SAHSU practice to reload datasets each time a fresh year is received to ensure the latest processing is uniformly applied to all data. SAHSU operates a hierarchy of data access permission based on user role: 1. General level access to aggregated health data, such as that publicly available from data providers websites e.g. district level mortality counts; 2. SAHSU researcher with access to small area data that is not pseudonymised and non-sensitive; 3. SAHSU researcher level access to pseudonymised sensitive data where required for specific projects; 4. Database team access to identifiable information supplied by data providers e.g. to pseudonymise the data. All access to confidential data is password protected. Data may only be extracted by the database team, or by using a “self-select” tool called Rapid Inquiry Facility (RIF). Note that this tool allows the user to select the nature of data to be extracted, but only at in an aggregated form. The RIF is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. This facility was initially designed as a tool for SAHSU staff to analyse routinely collected health data in relation to environmental exposures in the European Health and Environment Information System (EUROHEIS) project and has also been used by SAHSU to provide aggregated information as part of the US Centers for Disease Control (CDC) environmental public health tracking. In all cases data is extracted using the Rapid Enquiry Facility (RIF) or by the database team, and all extracts are supervised by the database manager. All data extracts are logged and cross checked by the database manager prior to extraction. The RIF will also permit data extraction and will also enforce identical checks. The checks carried out prior to data extraction are: • Projects is approved by the SAHSU liaison committee; • User is under contract to Imperial; • If required, access to event data (date of birth and/or death) has been justified; • SAHSU confidentiality form has been signed ; • The user has been information governance inducted and trained. In addition to the data provided by the HSCIC, SAHSU hold the following record level identifiable datasets: • ONS Births and Still births • ONS Cancer Incidence • Welsh Cancer Intelligence and Surveillance Unit • ONS Mortality • National Congenital Anomaly Register (NCAR from ONS) • Local Congenital Anomaly registries affiliated with BINOCAR • Terminations grounds “E” • NN4B • NCCHD (National Community Child Health Database) Linkage of data between datasets is only permitted with: • Approval via a substantial amendment to SAHSU ethics approval • Approval via a substantial amendment from HRA CAG • Explicit written permission from the data providers concerned. To date, amendments to the s.251 support have been sought and granted for the following three projects requiring specific data linkage: 1. Traffic pollution and health in London; 2. Incinerators; 3. Small area variation in coronary heart disease incidence, mortality and survival and their risk factors and determinants in England. Any projects requiring linkage beyond that covered by this application would also be subject to an amendment for DAAG’s consideration. It would also require support from ethics, and be covered by an amendment to the existing s251. Data minimisation As part of this application, the data required has been rationalised and HES data currently held by SAHSU no longer covered by this agreement will be securely destroyed. The remaining years are required by a number of studies, but the totality of years of data is also required - e.g. in a study relating to health effects in relation to environmental exposures from major airports. Whilst the initial study is complete, the data is required to be retained in order to respond to any queries relating to the research (a common requirement for published research). National data is also required in order to provide the ad hoc rapid response service to Public Health England. Given that PHE coverage is across England, and the requirements cannot be predicted in advance, national data is required given that response times do not permit time to request, receive and process individual extracts of data.	Specific outputs expected, including target date: There are three main types of output from this application :- 1. The maintenance of the research database, and associated record level pseudonymised extracts for use by individual researchers within SAHSU. Such extracts are delivered either through the RIF or the database team. This database will have the ability to provide data for the specific projects which have been approved as set out above. Note that all extracts are subject to the constraints within this agreement, eg: that data will only be held and processed at the processing / storage address. Such work is on-going through the lifetime of this agreement. 2. Individual project research outputs. All such outputs would be aggregate data, and be used within journal papers, research reports and results, presentations at conferences. Such research outputs would be placed into the public domain. Examples of previous outputs and projects are given below. 3. Individual analyses for Public Health England (PHE). Again, all such outputs would be in aggregated form, but would be provided directly to PHE. Examples of outputs produced to date -Traffic pollution and health in London study Recent papers published include: • Halonen, JI et al. Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. European Heart Journal 2015. • Gulliver, J et al. Development of an open-source road traffic noise model for exposure assessment. Environmental Modelling & Software. 2015. • Halonen JI et al. Is long -term exposure to traffic pollution associated with mortality? A spatial analysis in London. Environmental Pollution. 2015. Future outputs from the study will be disseminated via peer reviewed publication and academic conferences presentations. The outputs of the project will be published in peer-reviewed journals during the course of the study and after completion, which was expected by 2016 but will now be in 2017. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) There have been three papers published to date arising from work on this study: • Font A et al. Using Metal Ratios to Detect Emissions from Municipal Waste Incinerators in Ambient Air Pollution Data. Atmospheric Environment. 2015 • Ashworth DC, et al. Comparative assessment of particulate air pollution exposure from municipal solid waste incinerator emissions. Journal of Environmental and Public Health. 2013 • Ghosh R, et al. Ghosh RE, Ashworth DC, Hansell AL, Garwood K, Elliott P, Toledano MB. Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology. Arch Dis Child Fetal Neonatal Ed. 2016 Further papers are expected in 2017. Study information is provided on the SAHSU website for the public. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study have been published in the peer-reviewed BMJ (http://www.bmj.com/content/347/bmj.f5432 ). All outputs from individual projects will be anonymised (data will only be shared where aggregated with small numbers suppressed in line with the HES Analysis Guide). To confirm, no record level data is provided to any third party organisation and no commercial use is permitted.	Expected measurable benefits to health and/or social care including target date: The focus of the work under this application is to enable key public health issues associated with environmental factors at a small area. This would by definition therefore not look at care of individuals, but would related to informing public health policy and considering population health risks. The key points in demonstrating the benefit to health and social care therefore are :- - The core SAHSU programme of work is funded by Public Health England, and this includes the service to Public Health England to assist PHE in fulfilling their duties - Individual projects must meet be aligned with the Terms of Reference of the SAHSU programme, which includes addressing environmental and health issues. As part of this application, SAHSU will be amending these conditions to include an explicit requirement to reference that projects must be for the promotion of health - All projects are approved by the PHE Programme Board, which includes membership from DH. In order to assist with standardising approaches across application processes, SAHSU have offered membership of the approving group to the Data Access Request Service. Whilst individual detailed project related benefits cannot be stated at this time, three examples are given below of how outputs have and will be used to inform health policy. -Traffic pollution and health in London study The results of this study allow for a better understanding of the health problems caused by air pollution and noise from traffic in London. Findings to date have received a high media-profile and have the potential to influence air pollution policy and regulatory practices in London and the UK. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) The study was commissioned to extend the evidence base and to provide further information to the public about any potential reproductive and infant health risks from MWIs and to extend the evidence base with respect to exposures and any potential reproductive and infant health risks from MWIs. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study allow for better understanding of the health problems caused by noise from aircraft in London. The findings had a high media-profile and are directly relevant to the Davies commission and decisions on whether to build a third run-way at Heathrow. Results of this study have been reported widely in local, national and international media and been raised as questions at Prime-ministers question. The results of SAHSU studies are placed in the public domain via peer-reviewed publications and are used to inform the behaviour of health care providers and to inform national public health policy. All studies equally feedback their results into the relevant policy leads within PHE.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Critical Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Objective for processing: The Small Area Health Statistics Unit (SAHSU) is a long-standing and internationally-recognised centre of excellence assessing the risk of exposure to environmental pollutants to the health of the population, with an emphasis on the use and interpretation of routine health statistics at small-area level. SAHSU was established in 1987 as a recommendation of the Black enquiry into the incidence of leukaemia and lymphoma in children and young adults near the Windscale/Sellafield nuclear power plant. SAHSU has a particular role nationally in carrying out environmental health surveillance of the population in relation to environmental contaminants and point sources of industrial pollution, based on routinely collected health data. This is a highly specialised area of work requiring excellence in computing, database management, geographical information systems (GIS), statistics, environmental exposure assessment and epidemiology. The set of skills and expertise that has been established and built up in SAHSU is a unique resource both nationally and worldwide. SAHSU has a programme of work established which is defined by the following terms of reference :- 1) To develop and maintain databases of health data, environmental exposures as required to meet specific need, and social confounding factors at the small area level; 2) To carry out substantive research studies on environment and health issues including studies of the relationship between socio-economic factors and health, in collaboration with other scientific groups as necessary; 3) In collaboration with other scientific groups, to build up reliable background information on the distribution of environmental exposure, socio-economic data and disease amongst small areas; 4) To develop methodology for analysing and interpreting health outcomes related to small areas; 5) To act as a centre of expertise, disseminating information on developments in spatial epidemiological methods to national and regional groups; 6) To respond rapidly, with expert advice, to ad hoc queries from the core funding bodies (DH and PHE) about unusual clusters of disease, particularly in the neighbourhood of industrial installations To deliver against that Terms of Reference, SAHSU will utilise the data to meet the following purposes: Purpose 1 – maintenance of the SAHSU health research database At the core of the overall programme of work is the maintenance of the SAHSU research database, which combines multiple datasets and has both ethics approval and s251 approval in place Purpose 2 – to carry out a programme of research projects and studies. The research programme includes both methods development and investigation of priority questions in environment and health. A key aim is to improve the science base underlying translation of knowledge on the effects of the environment on health into policy. SAHSU conducts national research studies on environmental factors that may affect health ranging from exposures to electromagnetic fields (such as from electricity power lines) to traffic-related air pollution and noise, using nationally collected patient data including mortality, hospital admissions, cancer registrations and births data. Additional small area analyses are conducted that help support the general remit of the unit e.g. investigating differential hospital admission rates in ethnically diverse small areas. SAHSU provides national expertise in cluster and small area statistical methods and has close links with Public Health England including input into their environmental public health tracking programme and surveillance activities. Approval of individual projects All SAHSU studies are controlled via the PHE-SAHSU Liaison Committee. New study concepts must initially be approved by either the Director or Assistant director of SAHSU prior to the outline study proposal being created. Consideration is given to whether the study is adequately covered by SAHSU’s existing ethical approval and if not, separate ethical approval must be sought. Once ethical approval is confirmed the outline study proposal is reviewed by the SAHSU-PHE liaison committee who, once approved then take the study for formal minuted approval from the appropriate PHE programme board (attended by a member of the Department of Health). Projects are only approved where they are within the constraints of the SAHSU programme terms of reference. Purpose 3 – to provide rapid ad hoc support to PHE and DH about unusual clusters of disease, particularly in the neighbourhood of industrial installations The rapid response function is in our contract with Public Health England (i.e. mandated and funded by PHE). Such work is carried out on the instruction of DH or PHE, and is approved by the Director of SAHSU. By its nature, such requirements cannot be detailed in advance, but the only outputs would be aggregate with small numbers suppressed, and provided to DH and PHE. The Rapid Inquiry Facility (RIF) is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. A newer version of the Rapid Inquiry Facility (RIF) software is currently being developed. This version will allow us to test on various scenarios to help detect clusters or increased rates of disease near industrial installations. As a result of the RIF being redeveloped, we have been doing less on this function (due to less capacity/resources available) but we are expecting to be doing more over the next 1-2 years. As an example of the most recent request we have just responded to a request from the Committee on the Medical Aspects of Radiation in the Environment (COMARE) to use the RIF to provide ongoing surveillance of cancer around nuclear power stations using cancer registrations. SAHSU’s Welcome Trust seed award on “Public health surveillance of chronic diseases: suitability of spatio-temporal methods” starting June 2017 for two years will be testing capacity of HES and HES monthly to look at clusters and appropriate statistical methods to use which may vary by disease type. SAHSU have a single s251 support in place to cover the above. Any project which may have additional requirements (for example to require the linkage of an additional dataset beyond that previously agreed) must seek an amendment to the existing s251. It would also be outside the scope of this application, and therefore an amendment would be put before DAAG for consideration.	Processing activities: Processing is consistent across all three purposes, given that they all require the use of the same research database, In summary :- - identifiable data are encrypted and held in a secure area of the database on the SAHSU private network. Access to the identifiable data is limited to a small database team within SAHSU. - The identifiable data is held on a separate encrypted file system with access limited to the database team only. The pseudonymised output (CSV) file is then loaded into Oracle for use by researchers. - Separation is maintained between the database team, who handle data encryption and see identifiable data, and researchers, who only have access to pseudonymised data. NHS numbers, addresses and postcodes are encrypted and replaced by pseudonyms and held in confidential tables to which only the database team has access. Record separation occurs during data loading; the original files are stored on a separate encrypted file system on a separate server. Identifiers are not held on the same server as the clinical data. Further processing standardises names, data types, correct dates, links in geography via the postcode, performs encryption and pseudonymisation and carries out dataset specific bespoke processing (e.g. the detection of potential duplicates). At this point the data is only accessible by the database team, and is fully protected by encryption and pseudonymisation. The next phase creates production tables and sets them up for use by the researchers, granting appropriate permissions and setting up auditing. There then follows an extensive set of quality control checks; these are documented in the database. Finally, documentation is automatically generated. When the process is complete the load tables are dumped to the encrypted file system for reference and then removed. It is therefore practicable to reload SAHSU data at intervals to enhance security and to add processing improvements to pre-existing data (e.g. improved data processing, enhanced security, improved pseudonymisation). It is normal SAHSU practice to reload datasets each time a fresh year is received to ensure the latest processing is uniformly applied to all data. SAHSU operates a hierarchy of data access permission based on user role: 1. General level access to aggregated health data, such as that publicly available from data providers websites e.g. district level mortality counts; 2. SAHSU researcher with access to small area data that is not pseudonymised and non-sensitive; 3. SAHSU researcher level access to pseudonymised sensitive data where required for specific projects; 4. Database team access to identifiable information supplied by data providers e.g. to pseudonymise the data. All access to confidential data is password protected. Data may only be extracted by the database team, or by using a “self-select” tool called Rapid Inquiry Facility (RIF). Note that this tool allows the user to select the nature of data to be extracted, but only at in an aggregated form. The RIF is an automated tool that uses both database and Geographic Information System (GIS) technologies. The purpose of the RIF is to rapidly address epidemiological and public health questions using routinely collected health and population data. This allows SAHSU to respond rapidly, with expert advice to ad hoc queries from the funding departments about unusual clusters of disease, particularly in the neighbourhood of industrial installations. The RIF can perform risk analysis around putative hazardous sources and can be used for disease mapping. It generates standardised rates and relative risks for any given health outcome, for specified age and year ranges, for any given geographical area. This facility was initially designed as a tool for SAHSU staff to analyse routinely collected health data in relation to environmental exposures in the European Health and Environment Information System (EUROHEIS) project and has also been used by SAHSU to provide aggregated information as part of the US Centers for Disease Control (CDC) environmental public health tracking. In all cases data is extracted using the Rapid Enquiry Facility (RIF) or by the database team, and all extracts are supervised by the database manager. All data extracts are logged and cross checked by the database manager prior to extraction. The RIF will also permit data extraction and will also enforce identical checks. The checks carried out prior to data extraction are: • Projects is approved by the SAHSU liaison committee; • User is under contract to Imperial; • If required, access to event data (date of birth and/or death) has been justified; • SAHSU confidentiality form has been signed ; • The user has been information governance inducted and trained. In addition to the data provided by the HSCIC, SAHSU hold the following record level identifiable datasets: • ONS Births and Still births • ONS Cancer Incidence • Welsh Cancer Intelligence and Surveillance Unit • ONS Mortality • National Congenital Anomaly Register (NCAR from ONS) • Local Congenital Anomaly registries affiliated with BINOCAR • Terminations grounds “E” • NN4B • NCCHD (National Community Child Health Database) Linkage of data between datasets is only permitted with: • Approval via a substantial amendment to SAHSU ethics approval • Approval via a substantial amendment from HRA CAG • Explicit written permission from the data providers concerned. To date, amendments to the s.251 support have been sought and granted for the following three projects requiring specific data linkage: 1. Traffic pollution and health in London; 2. Incinerators; 3. Small area variation in coronary heart disease incidence, mortality and survival and their risk factors and determinants in England. Any projects requiring linkage beyond that covered by this application would also be subject to an amendment for DAAG’s consideration. It would also require support from ethics, and be covered by an amendment to the existing s251. Data minimisation As part of this application, the data required has been rationalised and HES data currently held by SAHSU no longer covered by this agreement will be securely destroyed. The remaining years are required by a number of studies, but the totality of years of data is also required - e.g. in a study relating to health effects in relation to environmental exposures from major airports. Whilst the initial study is complete, the data is required to be retained in order to respond to any queries relating to the research (a common requirement for published research). National data is also required in order to provide the ad hoc rapid response service to Public Health England. Given that PHE coverage is across England, and the requirements cannot be predicted in advance, national data is required given that response times do not permit time to request, receive and process individual extracts of data.	Specific outputs expected, including target date: There are three main types of output from this application :- 1. The maintenance of the research database, and associated record level pseudonymised extracts for use by individual researchers within SAHSU. Such extracts are delivered either through the RIF or the database team. This database will have the ability to provide data for the specific projects which have been approved as set out above. Note that all extracts are subject to the constraints within this agreement, eg: that data will only be held and processed at the processing / storage address. Such work is on-going through the lifetime of this agreement. 2. Individual project research outputs. All such outputs would be aggregate data, and be used within journal papers, research reports and results, presentations at conferences. Such research outputs would be placed into the public domain. Examples of previous outputs and projects are given below. 3. Individual analyses for Public Health England (PHE). Again, all such outputs would be in aggregated form, but would be provided directly to PHE. Examples of outputs produced to date -Traffic pollution and health in London study Recent papers published include: • Halonen, JI et al. Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. European Heart Journal 2015. • Gulliver, J et al. Development of an open-source road traffic noise model for exposure assessment. Environmental Modelling & Software. 2015. • Halonen JI et al. Is long -term exposure to traffic pollution associated with mortality? A spatial analysis in London. Environmental Pollution. 2015. Future outputs from the study will be disseminated via peer reviewed publication and academic conferences presentations. The outputs of the project will be published in peer-reviewed journals during the course of the study and after completion, which was expected by 2016 but will now be in 2017. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) There have been three papers published to date arising from work on this study: • Font A et al. Using Metal Ratios to Detect Emissions from Municipal Waste Incinerators in Ambient Air Pollution Data. Atmospheric Environment. 2015 • Ashworth DC, et al. Comparative assessment of particulate air pollution exposure from municipal solid waste incinerator emissions. Journal of Environmental and Public Health. 2013 • Ghosh R, et al. Ghosh RE, Ashworth DC, Hansell AL, Garwood K, Elliott P, Toledano MB. Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology. Arch Dis Child Fetal Neonatal Ed. 2016 Further papers are expected in 2017. Study information is provided on the SAHSU website for the public. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study have been published in the peer-reviewed BMJ (http://www.bmj.com/content/347/bmj.f5432 ). All outputs from individual projects will be anonymised (data will only be shared where aggregated with small numbers suppressed in line with the HES Analysis Guide). To confirm, no record level data is provided to any third party organisation and no commercial use is permitted.	Expected measurable benefits to health and/or social care including target date: The focus of the work under this application is to enable key public health issues associated with environmental factors at a small area. This would by definition therefore not look at care of individuals, but would related to informing public health policy and considering population health risks. The key points in demonstrating the benefit to health and social care therefore are :- - The core SAHSU programme of work is funded by Public Health England, and this includes the service to Public Health England to assist PHE in fulfilling their duties - Individual projects must meet be aligned with the Terms of Reference of the SAHSU programme, which includes addressing environmental and health issues. As part of this application, SAHSU will be amending these conditions to include an explicit requirement to reference that projects must be for the promotion of health - All projects are approved by the PHE Programme Board, which includes membership from DH. In order to assist with standardising approaches across application processes, SAHSU have offered membership of the approving group to the Data Access Request Service. Whilst individual detailed project related benefits cannot be stated at this time, three examples are given below of how outputs have and will be used to inform health policy. -Traffic pollution and health in London study The results of this study allow for a better understanding of the health problems caused by air pollution and noise from traffic in London. Findings to date have received a high media-profile and have the potential to influence air pollution policy and regulatory practices in London and the UK. -Possible reproductive and other health effects associated with Municipal Waste Incinerators (MWIs) in England, Wales and Scotland (incinerators) The study was commissioned to extend the evidence base and to provide further information to the public about any potential reproductive and infant health risks from MWIs and to extend the evidence base with respect to exposures and any potential reproductive and infant health risks from MWIs. -Health effects of large airports – the London Heathrow example (Heathrow) The results of this study allow for better understanding of the health problems caused by noise from aircraft in London. The findings had a high media-profile and are directly relevant to the Davies commission and decisions on whether to build a third run-way at Heathrow. Results of this study have been reported widely in local, national and international media and been raised as questions at Prime-ministers question. The results of SAHSU studies are placed in the public domain via peer-reviewed publications and are used to inform the behaviour of health care providers and to inform national public health policy. All studies equally feedback their results into the relevant policy leads within PHE.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	MRIS - Members and Postings Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The Airwave Health Monitoring Study was established in 2003 to evaluate possible health risks associated with the use of Terrestrial trunked radio (TETRA), a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. It addresses needs raised in a report by the Advisory Group on Non-Ionising Radiation (AGNIR) on the possible health effects from TETRA. There are currently c. 53,000 participants in the Study. The aim of the study is to estimate the risk of all cancers, certain mortality outcomes and various non-fatal, non-malignant health disorders in relation to Airwave use. As well as the focus on cancer incidence, the study will investigate non-cancer health outcomes (including cognitive, neuropsychiatric and neuro-degenerative effects which may be linked to sickness absence and early retirements), as the mechanisms of any putative health effect related to TETRA use are unknown. The cohort consists of police force employees from Great Britain and c. 53,000 participants are enrolled at the present. The study population will be flagged for mortality and cancer incidence using the cancer and mortality data at NHS Digital and Information Services Division (ISD) of the Scottish Health Service, based on personal identifiers such as full name, date of birth and NHS number.	NHS Digital data is only accessed by substantive employees of Imperial College London and only for the purposes described in this document. Directly identifiable data is kept separate from the study data, NHS Digital data is only linked to the AIRWAVE study data and no other datasets held by the applicant. The standard ONS terms and conditions will be adhered to. Airwave data from each police participant will be collected from monthly downloads of relevant data from the Home Office, giving information on Airwave exposure at individual level. These will be combined with questionnaire data about participants' use of Airwave to derive an exposure metric. Health outcome data will be assessed by linking information on individual participants to national records on mortality and cancer incidence, and from absence records supplied by the police force employers. Data, once received, is stored and analysed at Servers in South Kensington campus on servers located in a secured area. Users at St Marys connect to the servers via remote desktop. All IT infrastructure is owned and managed by Imperial college, there are no shared resources, and all network traffic is contained within Imperial College. The servers will be designated as holding identifiable data or anonymised data. All servers are secured to only accepting connections from specified users and workstations. Identifiable data can only be accessed from dedicated workstations that sit alongside the users college PC. These workstations can only be used to connect to the “identifiable servers”, there is no internet access available. Data uploading/downloading can be further restricted to specified users and PCs. All data transfers are recorded and kept for audit purposes by a staff member who has the role of “internal auditor”. The internal auditor monitors compliance of the Information Governance Policy including all agreements the groups have with external groups. The NHS data sharing agreement covers the Imperial college campus, and relies on each group having an environment that conforms to the NHS toolkit standard. Data received will be linked to the existing participant database. The scope of IGT policy whose code is EE133887-SPHTR (Imperial College London - School of Public Health Medical Trials and Research) is the Imperial College network. It uses the Imperial College infrastructure to create isolated enclaves that are used to form the security zones of the network. The IGT policy requires that, “Where possible all servers should be held within Imperial College’s Data centre, and subject to its security policy (currently aiming towards ISO27001). Any group not able to place a server in the datacentre will need to seek approval from the Security Manager.” The Airwave Study activities that are bound by EE133887-SPHTR will use servers located in the Data centre. Users working according to EE133887-SPHTR will be based at the College site in Norfolk Place and will access the Imperial College Data centre at South Kensington according to the security requirements defined in EE133887-SPHTR; the IGT policy therefore covers both the South Kensington and Norfolk Place sites. From time-to-time, consolidated pseudonymised extracts of the database are created and these are used by researchers to investigate the questions addressed by the Study. Those extracts follow the same security rules of the main database and will be kept in the same location. Other than in exceptional cases, namely resolving linkage questions or to contact research participants, data used by researchers is delinked from personal identifiers such as name and address. All researchers complete a detailed written confidentiality agreement with the College, and ONS Linkage Short Declaration of Use. When the Study is completed and closed to further analysis, the data will be archived securely during the life time of the data sharing agreement and for such time as is necessary to provide proper audit for published research. The data will be subsequently securely destroyed. No third parties will be allowed to access any data provided under this agreement. The applicant will supply NHS Digital with name, address (including postcode) and date of birth for linkage. This will ensure that any new members not already flagged by NHS Digital are linked and remove any members who have subsequently opted out of the study.	The aim of the study is to estimate the risk of all cancers and certain mortality outcomes in relation to Airwave use. Cancer and death notifications will be used to determine prevalent cases at baseline and subsequent incident cases for each outcome (e.g. head and neck cancers) under study. Survival analyses will be performed to investigate the association between each outcome and level of Tetra exposure and the risk of incident cases for each disease using multivariable Cox models. The results of the analyses will be published in peer-reviewed scientific journals and in summary form on the study website. The peer-reviewed journals targeted are likely to be similar to those that have already published in (Environmental Research). The data will be used to compile progress reports for the Study funder (the Home Office). However, all such outputs will report aggregated results only, and no individual will ever be identified. Results will be published on aggregate level with small numbers suppressed. It will not be possible to identify the individuals. The recruitment phase was completed on the 31st of March 2015. However, the follow up and data analysis phase continue. The target date for submission of a scientific outcomes paper (in a peer reviewed journal) on any possible long-term health implications for Police personnel related to use of Airwave (the main purpose of the Study) will be December 2017. Recent publications: 13th July 2016 Acute Exposure to Terrestrial Trunked Radio(TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation http://dx.doi.org/10.1016/j.envres.2016.06.031 28th April 2016 Validation of objective records and misreporting of personal radio use in a cohort of British police forces (the Airwave Health Monitoring Study) http://dx.doi.org/10.1016/j.envres.2016.04.018 06th September 2014 The Airwave Health Monitoring Study of police Officers and staff in Great Britain: Rationale, design and methods http://dx.doi.org/10.1016/j.envres.2014.07.025	• Safety of Airwave. The primary objective of the Study is to ascertain whether or not there is any link between use of Airwave and the long-term health of its users. Were such a link identified, it would be relevant and important to both Airwave users and management to understand in what circumstances risks might be increased. Alternatively, a finding of no demonstrable effect would be reassuring to the Airwave user community and would place any current and future concerns about possible health effects into proper context based on objective evidence. • Helping future generations. The study is generating new knowledge of benefit not only to police officers and staff as individuals, but to the wider community and to society as a whole. Analyses of data and samples will help to better understand the risks and causes of future diseases and ill health, and thus inform improved preventive and treatment strategies. There are many special aspects of the Airwave cohort including the occupational setting, given the particular nature of police duties and working patterns, the relatively young age of the cohort, and the inclusion of large numbers of women as well as men, which make this study uniquely valuable. The results generated from the use of this resource will inform future policy and practice both for the betterment of police force health and for the health of the public more generally. • Clarity in respect of health effects of long-term use of radio frequency technology. Continuing to gather the data necessary to undertake and report on the Study’s analyses of Airwave use and health will allow the potential long-term health effects to be better understood, and to place any future claims of possible harm into proper context based on the evidence. The findings would be relevant to, and inform, strategic decisions about future investment in radio communications systems within the Police Service. • Responsibility to the health and welfare of the workforce. Policing is a highly complex occupation with specific patterns of working and occupational risks with potential health effects that are not well understood. The Study has established a ‘broad and deep’ biomedical resource with which to continue to monitor the health and well-being of the workforce, and to help understand the causes and risks of ill-health and disease. Results will inform possible preventive approaches and best practice for maintenance of a healthy and engaged workforce.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The Airwave Health Monitoring Study was established in 2003 to evaluate possible health risks associated with the use of Terrestrial trunked radio (TETRA), a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. It addresses needs raised in a report by the Advisory Group on Non-Ionising Radiation (AGNIR) on the possible health effects from TETRA. There are currently c. 53,000 participants in the Study. The aim of the study is to estimate the risk of all cancers, certain mortality outcomes and various non-fatal, non-malignant health disorders in relation to Airwave use. As well as the focus on cancer incidence, the study will investigate non-cancer health outcomes (including cognitive, neuropsychiatric and neuro-degenerative effects which may be linked to sickness absence and early retirements), as the mechanisms of any putative health effect related to TETRA use are unknown. The cohort consists of police force employees from Great Britain and c. 53,000 participants are enrolled at the present. The study population will be flagged for mortality and cancer incidence using the cancer and mortality data at NHS Digital and Information Services Division (ISD) of the Scottish Health Service, based on personal identifiers such as full name, date of birth and NHS number.	NHS Digital data is only accessed by substantive employees of Imperial College London and only for the purposes described in this document. Directly identifiable data is kept separate from the study data, NHS Digital data is only linked to the AIRWAVE study data and no other datasets held by the applicant. The standard ONS terms and conditions will be adhered to. Airwave data from each police participant will be collected from monthly downloads of relevant data from the Home Office, giving information on Airwave exposure at individual level. These will be combined with questionnaire data about participants' use of Airwave to derive an exposure metric. Health outcome data will be assessed by linking information on individual participants to national records on mortality and cancer incidence, and from absence records supplied by the police force employers. Data, once received, is stored and analysed at Servers in South Kensington campus on servers located in a secured area. Users at St Marys connect to the servers via remote desktop. All IT infrastructure is owned and managed by Imperial college, there are no shared resources, and all network traffic is contained within Imperial College. The servers will be designated as holding identifiable data or anonymised data. All servers are secured to only accepting connections from specified users and workstations. Identifiable data can only be accessed from dedicated workstations that sit alongside the users college PC. These workstations can only be used to connect to the “identifiable servers”, there is no internet access available. Data uploading/downloading can be further restricted to specified users and PCs. All data transfers are recorded and kept for audit purposes by a staff member who has the role of “internal auditor”. The internal auditor monitors compliance of the Information Governance Policy including all agreements the groups have with external groups. The NHS data sharing agreement covers the Imperial college campus, and relies on each group having an environment that conforms to the NHS toolkit standard. Data received will be linked to the existing participant database. The scope of IGT policy whose code is EE133887-SPHTR (Imperial College London - School of Public Health Medical Trials and Research) is the Imperial College network. It uses the Imperial College infrastructure to create isolated enclaves that are used to form the security zones of the network. The IGT policy requires that, “Where possible all servers should be held within Imperial College’s Data centre, and subject to its security policy (currently aiming towards ISO27001). Any group not able to place a server in the datacentre will need to seek approval from the Security Manager.” The Airwave Study activities that are bound by EE133887-SPHTR will use servers located in the Data centre. Users working according to EE133887-SPHTR will be based at the College site in Norfolk Place and will access the Imperial College Data centre at South Kensington according to the security requirements defined in EE133887-SPHTR; the IGT policy therefore covers both the South Kensington and Norfolk Place sites. From time-to-time, consolidated pseudonymised extracts of the database are created and these are used by researchers to investigate the questions addressed by the Study. Those extracts follow the same security rules of the main database and will be kept in the same location. Other than in exceptional cases, namely resolving linkage questions or to contact research participants, data used by researchers is delinked from personal identifiers such as name and address. All researchers complete a detailed written confidentiality agreement with the College, and ONS Linkage Short Declaration of Use. When the Study is completed and closed to further analysis, the data will be archived securely during the life time of the data sharing agreement and for such time as is necessary to provide proper audit for published research. The data will be subsequently securely destroyed. No third parties will be allowed to access any data provided under this agreement. The applicant will supply NHS Digital with name, address (including postcode) and date of birth for linkage. This will ensure that any new members not already flagged by NHS Digital are linked and remove any members who have subsequently opted out of the study.	The aim of the study is to estimate the risk of all cancers and certain mortality outcomes in relation to Airwave use. Cancer and death notifications will be used to determine prevalent cases at baseline and subsequent incident cases for each outcome (e.g. head and neck cancers) under study. Survival analyses will be performed to investigate the association between each outcome and level of Tetra exposure and the risk of incident cases for each disease using multivariable Cox models. The results of the analyses will be published in peer-reviewed scientific journals and in summary form on the study website. The peer-reviewed journals targeted are likely to be similar to those that have already published in (Environmental Research). The data will be used to compile progress reports for the Study funder (the Home Office). However, all such outputs will report aggregated results only, and no individual will ever be identified. Results will be published on aggregate level with small numbers suppressed. It will not be possible to identify the individuals. The recruitment phase was completed on the 31st of March 2015. However, the follow up and data analysis phase continue. The target date for submission of a scientific outcomes paper (in a peer reviewed journal) on any possible long-term health implications for Police personnel related to use of Airwave (the main purpose of the Study) will be December 2017. Recent publications: 13th July 2016 Acute Exposure to Terrestrial Trunked Radio(TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation http://dx.doi.org/10.1016/j.envres.2016.06.031 28th April 2016 Validation of objective records and misreporting of personal radio use in a cohort of British police forces (the Airwave Health Monitoring Study) http://dx.doi.org/10.1016/j.envres.2016.04.018 06th September 2014 The Airwave Health Monitoring Study of police Officers and staff in Great Britain: Rationale, design and methods http://dx.doi.org/10.1016/j.envres.2014.07.025	• Safety of Airwave. The primary objective of the Study is to ascertain whether or not there is any link between use of Airwave and the long-term health of its users. Were such a link identified, it would be relevant and important to both Airwave users and management to understand in what circumstances risks might be increased. Alternatively, a finding of no demonstrable effect would be reassuring to the Airwave user community and would place any current and future concerns about possible health effects into proper context based on objective evidence. • Helping future generations. The study is generating new knowledge of benefit not only to police officers and staff as individuals, but to the wider community and to society as a whole. Analyses of data and samples will help to better understand the risks and causes of future diseases and ill health, and thus inform improved preventive and treatment strategies. There are many special aspects of the Airwave cohort including the occupational setting, given the particular nature of police duties and working patterns, the relatively young age of the cohort, and the inclusion of large numbers of women as well as men, which make this study uniquely valuable. The results generated from the use of this resource will inform future policy and practice both for the betterment of police force health and for the health of the public more generally. • Clarity in respect of health effects of long-term use of radio frequency technology. Continuing to gather the data necessary to undertake and report on the Study’s analyses of Airwave use and health will allow the potential long-term health effects to be better understood, and to place any future claims of possible harm into proper context based on the evidence. The findings would be relevant to, and inform, strategic decisions about future investment in radio communications systems within the Police Service. • Responsibility to the health and welfare of the workforce. Policing is a highly complex occupation with specific patterns of working and occupational risks with potential health effects that are not well understood. The Study has established a ‘broad and deep’ biomedical resource with which to continue to monitor the health and well-being of the workforce, and to help understand the causes and risks of ill-health and disease. Results will inform possible preventive approaches and best practice for maintenance of a healthy and engaged workforce.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The Airwave Health Monitoring Study was established in 2003 to evaluate possible health risks associated with the use of Terrestrial trunked radio (TETRA), a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. It addresses needs raised in a report by the Advisory Group on Non-Ionising Radiation (AGNIR) on the possible health effects from TETRA. There are currently c. 53,000 participants in the Study. The aim of the study is to estimate the risk of all cancers, certain mortality outcomes and various non-fatal, non-malignant health disorders in relation to Airwave use. As well as the focus on cancer incidence, the study will investigate non-cancer health outcomes (including cognitive, neuropsychiatric and neuro-degenerative effects which may be linked to sickness absence and early retirements), as the mechanisms of any putative health effect related to TETRA use are unknown. The cohort consists of police force employees from Great Britain and c. 53,000 participants are enrolled at the present. The study population will be flagged for mortality and cancer incidence using the cancer and mortality data at NHS Digital and Information Services Division (ISD) of the Scottish Health Service, based on personal identifiers such as full name, date of birth and NHS number.	NHS Digital data is only accessed by substantive employees of Imperial College London and only for the purposes described in this document. Directly identifiable data is kept separate from the study data, NHS Digital data is only linked to the AIRWAVE study data and no other datasets held by the applicant. The standard ONS terms and conditions will be adhered to. Airwave data from each police participant will be collected from monthly downloads of relevant data from the Home Office, giving information on Airwave exposure at individual level. These will be combined with questionnaire data about participants' use of Airwave to derive an exposure metric. Health outcome data will be assessed by linking information on individual participants to national records on mortality and cancer incidence, and from absence records supplied by the police force employers. Data, once received, is stored and analysed at Servers in South Kensington campus on servers located in a secured area. Users at St Marys connect to the servers via remote desktop. All IT infrastructure is owned and managed by Imperial college, there are no shared resources, and all network traffic is contained within Imperial College. The servers will be designated as holding identifiable data or anonymised data. All servers are secured to only accepting connections from specified users and workstations. Identifiable data can only be accessed from dedicated workstations that sit alongside the users college PC. These workstations can only be used to connect to the “identifiable servers”, there is no internet access available. Data uploading/downloading can be further restricted to specified users and PCs. All data transfers are recorded and kept for audit purposes by a staff member who has the role of “internal auditor”. The internal auditor monitors compliance of the Information Governance Policy including all agreements the groups have with external groups. The NHS data sharing agreement covers the Imperial college campus, and relies on each group having an environment that conforms to the NHS toolkit standard. Data received will be linked to the existing participant database. The scope of IGT policy whose code is EE133887-SPHTR (Imperial College London - School of Public Health Medical Trials and Research) is the Imperial College network. It uses the Imperial College infrastructure to create isolated enclaves that are used to form the security zones of the network. The IGT policy requires that, “Where possible all servers should be held within Imperial College’s Data centre, and subject to its security policy (currently aiming towards ISO27001). Any group not able to place a server in the datacentre will need to seek approval from the Security Manager.” The Airwave Study activities that are bound by EE133887-SPHTR will use servers located in the Data centre. Users working according to EE133887-SPHTR will be based at the College site in Norfolk Place and will access the Imperial College Data centre at South Kensington according to the security requirements defined in EE133887-SPHTR; the IGT policy therefore covers both the South Kensington and Norfolk Place sites. From time-to-time, consolidated pseudonymised extracts of the database are created and these are used by researchers to investigate the questions addressed by the Study. Those extracts follow the same security rules of the main database and will be kept in the same location. Other than in exceptional cases, namely resolving linkage questions or to contact research participants, data used by researchers is delinked from personal identifiers such as name and address. All researchers complete a detailed written confidentiality agreement with the College, and ONS Linkage Short Declaration of Use. When the Study is completed and closed to further analysis, the data will be archived securely during the life time of the data sharing agreement and for such time as is necessary to provide proper audit for published research. The data will be subsequently securely destroyed. No third parties will be allowed to access any data provided under this agreement. The applicant will supply NHS Digital with name, address (including postcode) and date of birth for linkage. This will ensure that any new members not already flagged by NHS Digital are linked and remove any members who have subsequently opted out of the study.	The aim of the study is to estimate the risk of all cancers and certain mortality outcomes in relation to Airwave use. Cancer and death notifications will be used to determine prevalent cases at baseline and subsequent incident cases for each outcome (e.g. head and neck cancers) under study. Survival analyses will be performed to investigate the association between each outcome and level of Tetra exposure and the risk of incident cases for each disease using multivariable Cox models. The results of the analyses will be published in peer-reviewed scientific journals and in summary form on the study website. The peer-reviewed journals targeted are likely to be similar to those that have already published in (Environmental Research). The data will be used to compile progress reports for the Study funder (the Home Office). However, all such outputs will report aggregated results only, and no individual will ever be identified. Results will be published on aggregate level with small numbers suppressed. It will not be possible to identify the individuals. The recruitment phase was completed on the 31st of March 2015. However, the follow up and data analysis phase continue. The target date for submission of a scientific outcomes paper (in a peer reviewed journal) on any possible long-term health implications for Police personnel related to use of Airwave (the main purpose of the Study) will be December 2017. Recent publications: 13th July 2016 Acute Exposure to Terrestrial Trunked Radio(TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation http://dx.doi.org/10.1016/j.envres.2016.06.031 28th April 2016 Validation of objective records and misreporting of personal radio use in a cohort of British police forces (the Airwave Health Monitoring Study) http://dx.doi.org/10.1016/j.envres.2016.04.018 06th September 2014 The Airwave Health Monitoring Study of police Officers and staff in Great Britain: Rationale, design and methods http://dx.doi.org/10.1016/j.envres.2014.07.025	• Safety of Airwave. The primary objective of the Study is to ascertain whether or not there is any link between use of Airwave and the long-term health of its users. Were such a link identified, it would be relevant and important to both Airwave users and management to understand in what circumstances risks might be increased. Alternatively, a finding of no demonstrable effect would be reassuring to the Airwave user community and would place any current and future concerns about possible health effects into proper context based on objective evidence. • Helping future generations. The study is generating new knowledge of benefit not only to police officers and staff as individuals, but to the wider community and to society as a whole. Analyses of data and samples will help to better understand the risks and causes of future diseases and ill health, and thus inform improved preventive and treatment strategies. There are many special aspects of the Airwave cohort including the occupational setting, given the particular nature of police duties and working patterns, the relatively young age of the cohort, and the inclusion of large numbers of women as well as men, which make this study uniquely valuable. The results generated from the use of this resource will inform future policy and practice both for the betterment of police force health and for the health of the public more generally. • Clarity in respect of health effects of long-term use of radio frequency technology. Continuing to gather the data necessary to undertake and report on the Study’s analyses of Airwave use and health will allow the potential long-term health effects to be better understood, and to place any future claims of possible harm into proper context based on the evidence. The findings would be relevant to, and inform, strategic decisions about future investment in radio communications systems within the Police Service. • Responsibility to the health and welfare of the workforce. Policing is a highly complex occupation with specific patterns of working and occupational risks with potential health effects that are not well understood. The Study has established a ‘broad and deep’ biomedical resource with which to continue to monitor the health and well-being of the workforce, and to help understand the causes and risks of ill-health and disease. Results will inform possible preventive approaches and best practice for maintenance of a healthy and engaged workforce.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Imperial College London’s Big Data and Analytical Unit (BDAU) requires an extract of HES data with linked month and year of death (where applicable) for use in a research study: ’ Evaluating the Rate of Deadoption of Interval Cholecystectomy, a Low Value Intervention, and Diffusion of Index Cholecystectomy, a High Value Intervention’. This study aims to investigate the respective values of two ways of treating patients with the same specific health condition. This study is being undertaken by a PhD student within Imperial College London and will contribute to a PhD thesis in addition to other outputs intended to maximise the benefit of the work. The Big Data and Analytical Unit (BDAU) is a multidiscipline team within Imperial College London which collaborates with a large network of researchers across the college with the aim of ensuring the maximum use, impact and dissemination of research using healthcare data. Finding efficiency savings in health care provision is paramount given the pressures on national health care budgets worldwide. This provides motivation to identify and reduce the use of health care interventions that deliver only marginal benefits, be it through overuse, misuse or waste, that could be substituted by less costly alternatives without affecting safety and quality of care. A greater emphasis on value is key and achieving high value for patients must become the goal of health care delivery. A clinical definition of low value interventions has been established as care in the absence of a clear medical basis for use or when the benefit of therapy does not outweigh risks; this encompasses terms such as medical overuse and over-diagnosis. The importance of identifying and studying low value healthcare services is motivated by the concept of ‘opportunity cost’, i.e. that disinvestments in low value procedures and services from the healthcare budget leads to the opportunity for further investments in higher value services. That is, a reduction in low value service results in improved value of care overall. This study aims to investigate the relationship between two interventions for a cholecystectomy – a surgical procedure to remove the gallbladder. Interval cholecystectomy is a low value intervention, while the other, index cholecystectomy is considered a high value intervention. Interval cholecystectomy is the choice to discharge patients following index admission and readmit them for an elective operation whereas index cholecystectomy is performed during index admission. These two interventions would be analysed to inspect the patterns of deadoption and adoption respectively. Cost analysis will be used to compare the two interventions and this will take into account the impact of adverse events, readmissions and excess mortality to ensure that costs and impacts are both analysed. These analyses will then inform outputs which will be used to help change current practices and improve patient care in respect of this particular condition. The study requires the hospital admissions data of any individual who had a procedure (defined by specific procedure codes) indicating a cholecystectomy treated by trusts which had 10 or more operations per year for the relevant procedure codes. Details of all hospital admissions for these individuals over a period of up to 10 years will be required because the study will take into account the possible relationships between the cholecystectomy and other admissions to ascertain the true costs of each type of intervention.	NHS Digital will securely transfer a pseudonymised extract of HES data and linked month and year of death to Imperial College London. Imperial College London will store the data on a server in the BDAU Secure Environment (SE). Data access is strictly controlled by the BDAU through a robust dataset registration process. No one other than BDAU staff can authorise access to the data. Access to the data will be restricted to one researcher, a PhD student, and that researcher’s supervisors if necessary (usually not required), only for the purpose outlined in this Data Sharing Agreement. The student and the supervisors are bound to the policies, procedures and equivalent controls of the BDAU SE and Imperial College London as substantive employees of the College. The raw data provided by NHS Digital will be analysed solely in the BDAU SE. Any further analysis done outside the BDAU SE (usually for visualisation purposes for output) will be done using data that has been aggregated with small numbers suppressed in line with the HES Analysis Guide. The data will be analysed to investigate the two interventions and associated cost, utilisation and outcome patterns. This will involve statistical analysis using standard and innovative statistical programmes inside the BDAU SE. Cost data sourced from the freely available ‘National Schedule of Reference Costs’ will be integrated into the raw dataset on an intervention level. Each intervention will be costed according to its relevant healthcare resource group (HRG); which is a reimbursement tariff of the average unit cost to the NHS of providing a defined service in a given financial year. This will not increase the risk of re-identifying individuals. Results will be graphed and compared at an aggregate level. The importance of longitudinal data (10 years) in this scenario is to capture the change in clinician & institution behaviour following the publication of a Cochrane review in 2006 (Gurusamy et al. 2006) which recognised interval cholecystectomy as being low value. Today interval cholecystectomy is still being used despite growing evidence to the contrary. The goal of creating a model of deadoption requires longitudinal data in order to illicit the changes in rates of use and to identify whether changes have been sustained. Without longitudinal observation an incomplete picture would be shown and the study would be unable to formulate recommendations to supply side policy change which is an ambition of this project. there will be no linkage with other record level data and no attempt to re-identify the data.	The following outputs will be produced: Models: Q3/2018 - A model for efficient de-adoption will be developed as part of this study Publications: It is intended that this study will lead to the following peer-reviewed publications which will be targeted for Health Affairs, the Lancet and the BMJ: Q3/2018 - Modelling of deadoption of low value procedures Q3/2018 - Adoption of high value procedures and geographical network analysis of diffusion of innovation Q3/2018 - Cost implications of non-deadoption of low value procedures Presentations: It is intended that this study will lead to presentations at the following conferences: Q2/2018 - The Association of Surgeons of Great Britain and Ireland - surgical conference Q2/2018 - Health + Care, Commissioning in Healthcare - conferences directed at healthcare commissioners Q3/2018 - The Association of Upper Gastrointestinal Surgeons - surgical conference Q3/2018 - Road to Rightcare, Overuse Conferences, World Congress on Health Economics - academic meetings Academic outputs: This study will contribute to a PhD thesis which will be published online. Target audience: The outputs of this study are aimed at those who will make use of the findings to decide the best course of care for patients. This includes surgeons who would be performing these operations, clinical commissioners who decide on priorities for funding and healthcare leads who can influence guidelines. This study is part of the Centre for Health Policy at Imperial College London which helps advise on global health policy, the Patient Safety Translational Research Centre which is one of 3 centres in the UK which translates research into clinical practice and the Global Health and Development Group which were formally part of NICE International which helped advise for local and global standards for clinical practice. All data which is used for outputs will be anonymous summary aggregate data. All outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide. No raw data will be transferred outside the BDAU SE and neither the data nor outputs will be used for commercial purposes.	By highlighting the differences in cost associated with the different treatments to key decision makers through the dissemination strategy outlined above, it is the hope and expectation that decisions will be taken to adopt the intervention type that offers the highest value resulting in potentially significant cost savings. Savings of £820 per patient with index cholecystectomy have been estimated (Gutt CN et al. 2013). With 72,572 (http://www.rcseng.ac.uk/healthcare-bodies/nscc/data-tools) non-operative admissions with gallstone disease in 2014, the potential for savings of £59,509,040 exists. Therefore, the opportunity cost of reallocating resources towards higher value services is great and, although this work will not guarantee such efficiency savings, it will contribute to beginning conversations with policy makers and clinicians to optimise treatment and begin change management. This conversation would utilise evidence produced by this work. This project would not only provide knowledge of the cost of persistent interval cholecystectomy but also an understanding of how best to promote a change to index cholecystectomy. By providing a novel model of efficient de-adoption (which would be a specific output of this research) potential benefits may be extended to other low value procedures both surgical (e.g. arthroscopy in osteoarthritis) and non-surgical (e.g. use of antibiotics when not indicated.) The aim with this work is to explore the practice, purpose and experience of deadoption and to develop new tools and insights to help guide those trying to navigate this space. The expectation is that the papers would be published by October 2018, thereby impacting clinical activity by October 2019.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The Airwave Health Monitoring Study was established in 2003 to evaluate possible health risks associated with the use of Terrestrial trunked radio (TETRA), a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. It addresses needs raised in a report by the Advisory Group on Non-Ionising Radiation (AGNIR) on the possible health effects from TETRA. There are currently c. 53,000 participants in the Study. The aim of the study is to estimate the risk of all cancers, certain mortality outcomes and various non-fatal, non-malignant health disorders in relation to Airwave use. As well as the focus on cancer incidence, the study will investigate non-cancer health outcomes (including cognitive, neuropsychiatric and neuro-degenerative effects which may be linked to sickness absence and early retirements), as the mechanisms of any putative health effect related to TETRA use are unknown. The cohort consists of police force employees from Great Britain and c. 53,000 participants are enrolled at the present. The study population will be flagged for mortality and cancer incidence using the cancer and mortality data at NHS Digital and Information Services Division (ISD) of the Scottish Health Service, based on personal identifiers such as full name, date of birth and NHS number.	NHS Digital data is only accessed by substantive employees of Imperial College London and only for the purposes described in this document. Directly identifiable data is kept separate from the study data, NHS Digital data is only linked to the AIRWAVE study data and no other datasets held by the applicant. The standard ONS terms and conditions will be adhered to. Airwave data from each police participant will be collected from monthly downloads of relevant data from the Home Office, giving information on Airwave exposure at individual level. These will be combined with questionnaire data about participants' use of Airwave to derive an exposure metric. Health outcome data will be assessed by linking information on individual participants to national records on mortality and cancer incidence, and from absence records supplied by the police force employers. Data, once received, is stored and analysed at Servers in South Kensington campus on servers located in a secured area. Users at St Marys connect to the servers via remote desktop. All IT infrastructure is owned and managed by Imperial college, there are no shared resources, and all network traffic is contained within Imperial College. The servers will be designated as holding identifiable data or anonymised data. All servers are secured to only accepting connections from specified users and workstations. Identifiable data can only be accessed from dedicated workstations that sit alongside the users college PC. These workstations can only be used to connect to the “identifiable servers”, there is no internet access available. Data uploading/downloading can be further restricted to specified users and PCs. All data transfers are recorded and kept for audit purposes by a staff member who has the role of “internal auditor”. The internal auditor monitors compliance of the Information Governance Policy including all agreements the groups have with external groups. The NHS data sharing agreement covers the Imperial college campus, and relies on each group having an environment that conforms to the NHS toolkit standard. Data received will be linked to the existing participant database. The scope of IGT policy whose code is EE133887-SPHTR (Imperial College London - School of Public Health Medical Trials and Research) is the Imperial College network. It uses the Imperial College infrastructure to create isolated enclaves that are used to form the security zones of the network. The IGT policy requires that, “Where possible all servers should be held within Imperial College’s Data centre, and subject to its security policy (currently aiming towards ISO27001). Any group not able to place a server in the datacentre will need to seek approval from the Security Manager.” The Airwave Study activities that are bound by EE133887-SPHTR will use servers located in the Data centre. Users working according to EE133887-SPHTR will be based at the College site in Norfolk Place and will access the Imperial College Data centre at South Kensington according to the security requirements defined in EE133887-SPHTR; the IGT policy therefore covers both the South Kensington and Norfolk Place sites. From time-to-time, consolidated pseudonymised extracts of the database are created and these are used by researchers to investigate the questions addressed by the Study. Those extracts follow the same security rules of the main database and will be kept in the same location. Other than in exceptional cases, namely resolving linkage questions or to contact research participants, data used by researchers is delinked from personal identifiers such as name and address. All researchers complete a detailed written confidentiality agreement with the College, and ONS Linkage Short Declaration of Use. When the Study is completed and closed to further analysis, the data will be archived securely during the life time of the data sharing agreement and for such time as is necessary to provide proper audit for published research. The data will be subsequently securely destroyed. No third parties will be allowed to access any data provided under this agreement. The applicant will supply NHS Digital with name, address (including postcode) and date of birth for linkage. This will ensure that any new members not already flagged by NHS Digital are linked and remove any members who have subsequently opted out of the study.	The aim of the study is to estimate the risk of all cancers and certain mortality outcomes in relation to Airwave use. Cancer and death notifications will be used to determine prevalent cases at baseline and subsequent incident cases for each outcome (e.g. head and neck cancers) under study. Survival analyses will be performed to investigate the association between each outcome and level of Tetra exposure and the risk of incident cases for each disease using multivariable Cox models. The results of the analyses will be published in peer-reviewed scientific journals and in summary form on the study website. The peer-reviewed journals targeted are likely to be similar to those that have already published in (Environmental Research). The data will be used to compile progress reports for the Study funder (the Home Office). However, all such outputs will report aggregated results only, and no individual will ever be identified. Results will be published on aggregate level with small numbers suppressed. It will not be possible to identify the individuals. The recruitment phase was completed on the 31st of March 2015. However, the follow up and data analysis phase continue. The target date for submission of a scientific outcomes paper (in a peer reviewed journal) on any possible long-term health implications for Police personnel related to use of Airwave (the main purpose of the Study) will be December 2017. Recent publications: 13th July 2016 Acute Exposure to Terrestrial Trunked Radio(TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation http://dx.doi.org/10.1016/j.envres.2016.06.031 28th April 2016 Validation of objective records and misreporting of personal radio use in a cohort of British police forces (the Airwave Health Monitoring Study) http://dx.doi.org/10.1016/j.envres.2016.04.018 06th September 2014 The Airwave Health Monitoring Study of police Officers and staff in Great Britain: Rationale, design and methods http://dx.doi.org/10.1016/j.envres.2014.07.025	• Safety of Airwave. The primary objective of the Study is to ascertain whether or not there is any link between use of Airwave and the long-term health of its users. Were such a link identified, it would be relevant and important to both Airwave users and management to understand in what circumstances risks might be increased. Alternatively, a finding of no demonstrable effect would be reassuring to the Airwave user community and would place any current and future concerns about possible health effects into proper context based on objective evidence. • Helping future generations. The study is generating new knowledge of benefit not only to police officers and staff as individuals, but to the wider community and to society as a whole. Analyses of data and samples will help to better understand the risks and causes of future diseases and ill health, and thus inform improved preventive and treatment strategies. There are many special aspects of the Airwave cohort including the occupational setting, given the particular nature of police duties and working patterns, the relatively young age of the cohort, and the inclusion of large numbers of women as well as men, which make this study uniquely valuable. The results generated from the use of this resource will inform future policy and practice both for the betterment of police force health and for the health of the public more generally. • Clarity in respect of health effects of long-term use of radio frequency technology. Continuing to gather the data necessary to undertake and report on the Study’s analyses of Airwave use and health will allow the potential long-term health effects to be better understood, and to place any future claims of possible harm into proper context based on the evidence. The findings would be relevant to, and inform, strategic decisions about future investment in radio communications systems within the Police Service. • Responsibility to the health and welfare of the workforce. Policing is a highly complex occupation with specific patterns of working and occupational risks with potential health effects that are not well understood. The Study has established a ‘broad and deep’ biomedical resource with which to continue to monitor the health and well-being of the workforce, and to help understand the causes and risks of ill-health and disease. Results will inform possible preventive approaches and best practice for maintenance of a healthy and engaged workforce.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Imperial College London Doctor Foster Unit (ICL DFU) currently uses HES data to identify measures of quality and safety in healthcare. DFU’s research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. The other organisation involved in this work is Dr Foster Limited (DFI). ICL DFU works in collaboration with DFI to provide a management information function in the form of analysis for healthcare organisations. ICL DFU is submitting this renewal to continue this work and to continue to providing a service to healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require continued access to HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. The datasets required from NHS Digital by ICL DFU and DFI are: • Admitted Patient Care • Accident and Emergency • Critical Care • Outpatients The full HES datasets are required to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard case mix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with fewer fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from Dr Foster Limited. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to Dr Foster Limited. The unit works in collaboration with Dr Foster Limited to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, Dr Foster Limited responds to customer requests for analyses of NHS Digital's data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the Dr Foster Limited's Head of Information Governance or SIRO will provide guidance and if required contact NHS Digital. Dr Foster Limited also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by Dr Foster Limited or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. Dr Foster Limited is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence. All persons accessing the data are substantive employees of Imperial Collage London.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005.	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Imperial College London Doctor Foster Unit (ICL DFU) currently uses HES data to identify measures of quality and safety in healthcare. DFU’s research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. The other organisation involved in this work is Dr Foster Limited (DFI). ICL DFU works in collaboration with DFI to provide a management information function in the form of analysis for healthcare organisations. ICL DFU is submitting this renewal to continue this work and to continue to providing a service to healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require continued access to HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. The datasets required from NHS Digital by ICL DFU and DFI are: • Admitted Patient Care • Accident and Emergency • Critical Care • Outpatients The full HES datasets are required to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard case mix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with fewer fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from Dr Foster Limited. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to Dr Foster Limited. The unit works in collaboration with Dr Foster Limited to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, Dr Foster Limited responds to customer requests for analyses of NHS Digital's data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the Dr Foster Limited's Head of Information Governance or SIRO will provide guidance and if required contact NHS Digital. Dr Foster Limited also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by Dr Foster Limited or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. Dr Foster Limited is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence. All persons accessing the data are substantive employees of Imperial Collage London.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005.	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Imperial College London Doctor Foster Unit (ICL DFU) currently uses HES data to identify measures of quality and safety in healthcare. DFU’s research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. The other organisation involved in this work is Dr Foster Limited (DFI). ICL DFU works in collaboration with DFI to provide a management information function in the form of analysis for healthcare organisations. ICL DFU is submitting this renewal to continue this work and to continue to providing a service to healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require continued access to HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. The datasets required from NHS Digital by ICL DFU and DFI are: • Admitted Patient Care • Accident and Emergency • Critical Care • Outpatients The full HES datasets are required to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard case mix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with fewer fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from Dr Foster Limited. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to Dr Foster Limited. The unit works in collaboration with Dr Foster Limited to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, Dr Foster Limited responds to customer requests for analyses of NHS Digital's data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the Dr Foster Limited's Head of Information Governance or SIRO will provide guidance and if required contact NHS Digital. Dr Foster Limited also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by Dr Foster Limited or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. Dr Foster Limited is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence. All persons accessing the data are substantive employees of Imperial Collage London.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005.	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMPERIAL COLLEGE LONDON	IMPERIAL COLLEGE LONDON	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	Imperial College London Doctor Foster Unit (ICL DFU) currently uses HES data to identify measures of quality and safety in healthcare. DFU’s research themes are around developing and validating indicators of quality and safety of healthcare, particularly by GP practice, consultant, and NHS Trust, showing variations in performance by unit, patient risk subgroups and risk prediction, risk adjustment and outlier detection for such indicators and variations and any other methodological aspects as they arise. The other organisation involved in this work is Dr Foster Limited (DFI). ICL DFU works in collaboration with DFI to provide a management information function in the form of analysis for healthcare organisations. ICL DFU is submitting this renewal to continue this work and to continue to providing a service to healthcare organisations. ICL DFU calculate a wide range of healthcare indicators (over 100) and as such require continued access to HES data to provide a wide array of relevant indicators to give end users as complete a picture of hospital performance as possible to allow UK healthcare and Social care organisations to effectively: • Monitor quality of services provided • Identify efficiency opportunities • Identify pathways where services can be improved for the benefit of patients A data period of 15 years of historical data is essential to enable both ICL DFU and Dr Foster Limited to: 1. Obtain longitudinal data on prior admissions for patients. Risk modelling will also require access to variables on prior admissions including previously recorded co-morbidities. 2. Create, update and maintain statistical risk models to enable the regular production of risk adjusted measures of mortality, quality and efficiency (including HSMR and Cusum alerts as used by NHS organisations and regulators) An example of a longitudinal study which ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. The datasets required from NHS Digital by ICL DFU and DFI are: • Admitted Patient Care • Accident and Emergency • Critical Care • Outpatients The full HES datasets are required to increase the power of predictive models for rare diseases, procedures and events (e.g. ICL DFU and DFI build standard case mix adjustment models for 259 diagnosis groups and 200 procedure groups which include some rarer conditions). At a high level the analyses break down into the following: • Quality measures of healthcare services by providers/area/clinical interest/trend analysis • Variations in health outcomes • Health inequalities and needs analysis • Predictions • Performance data and changes in clinical practice • Management information • Efficiency Monitoring • Benchmarking • Contract Management and Variance Analysis • Activity Monitoring • National Target Performance • Pathway design, redesign and improvement. • Practice Performance Monitoring • Capacity and utilisation management • Cross checking of commissioning data • Systems to support and monitor the pattern of healthcare usage • Overall data quality A bespoke extract with fewer fields and lesser frequency will not suffice given that ICL DFU/DFI require the most up-to-date information to inform trusts of potential issues around quality. A soon-to-be-published NIHR-funded review of a subset of mortality alerts sent between 2011 and 2013 (and subsequently followed up by CQC), found that Trusts reported areas of care that could be improved in 70% (108/154) of the alerts and that all were implementing action plans to address these issues. ICL DFU/DFI research has found on average, an associated reduction in mortality of 55% in the 12 months following a notified alert, suggesting timeliness of data may be key to saving lives. Sensitive fields Sensitive fields will only be available at a record level to NHS Provider Trusts (or approved regulatory bodies with express authority to demand such data, e.g. the CQC) and are specifically required for the purpose of conducting root cause analysis where there is a legitimate relationship with the patient. Where a legitimate relationship does not exist data will be available at an aggregate level in line with HSCIC HES Analysis Guide, HSCIC Small Numbers Procedure and ONS Guidelines, with any sensitive fields suppressed. Consultant Code ICL DFU and DFI provide consultancy from their analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. Analyses by consultant activity are fed back to the NHS through a range of Management Information Systems provided by DFI in the forms of aggregation of teams into 'departments' or other hierarchies. Requirements for analyses by consultant activity are consistent with NHS needs and policy direction (to publish at consultant level). Consultant code is also used in research e.g. analysing volume and outcome relations for elective surgery. Some exclusions are applied e.g. Invalid codes, dental consultant etc. Patient’s general medical practitioner Patient’s general medical practitioner is used to examine variations by GP practice and to enable mapping to practice level such as The Quality and Outcomes Framework (QOF) and practice staffing data etc. NHS Provider Trusts are able to identify the registered GP who referred the patient. This is essential to understanding rates of admission and rates of readmission by GP practice which may reflect issues of community and primary care. Person referring patient Analyses by the person referring patient activities are fed back to the NHS Provider Trusts through a range of Management Information Systems provided by Dr Foster Limited. These analyses allow NHS Provider Trusts to identify the person who referred the patient for calculation of referral rates. Understanding referral rates by GP practice and consultant can help to identify issues of quality of primary care. ICL DFU is part-funded by a grant from Dr Foster Limited. On approval of this application, a sub-licence model between the HSCIC and Imperial College will exist to permit ICL DFU to supply derived pseudonymised data together with specific clear text sensitive fields (as stated within this application) to Dr Foster Limited. The unit works in collaboration with Dr Foster Limited to provide a management information function in the form Dr Foster Analysis Toolkit. This purpose is fulfilled by analysis of HES data made available to customers via the following services provided by DFI: 1. Licensed subscriber of Dr Foster Analysis Toolkit a. Directly – i. NHS Provider Trust holding a subscription to the Dr Foster Analysis Toolkit are able to view data at a record level, with an option to use the patient re-identification service for approved individuals; or ii. other NHS organisations holding a subscription to Dr Foster Analysis Toolkit are able to view aggregated analysis to prevent any patients being identified in accordance with guidance provided by HSCIC. b. Indirectly – non-NHS organisation that hold a subscription to the tool supply NHS organisations with aggregate small number suppressed analyses. 2. Value Added Services As an information intermediary, Dr Foster Limited responds to customer requests for analyses of NHS Digital's data, whose scopes are by their nature bespoke and customised to local needs. An established specialist team of Analysts provides statistical analysis for interpreting complex data and producing analysis on behalf of customers. It should be stated that this team, which is project based, conduct annual training on handling sensitive records and are highly conversant in national guidelines to protect patient confidentiality, where there is any doubt the Dr Foster Limited's Head of Information Governance or SIRO will provide guidance and if required contact NHS Digital. Dr Foster Limited also provides analysis for publication for the benefit of the public and NHS e.g. Hospital Guide, and to support benefit to health and social care. Such analytical content may be published directly by Dr Foster Limited or within academic journals or articles to journalistic/media entities in the form of text, tables, and other data visualisation such as diagrams/graphs using aggregate information based on HES analysis. Dr Foster Limited is aware that publications, whether inside or outside the NHS, must adhere to strict guidelines in terms of disclosure, and will ensure any such publications are aggregated and comply with small number suppression in line with the HES Analysis Guide/ONS Guidelines and other relevant legislation and standards as defined in the Terms and Conditions of the Data Sharing Agreement.	Imperial College London Dr Foster Unit (ICL DFU) uses hospital administrative data in the form of HES bespoke/monthly extracts to identify measures of quality and safety of healthcare. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections, mortality and safety indicators. ICL DFU holds 2 databases to store data – A Research database and a Patient Identifiable database to provide a Re-Identification service for NHS provider trusts. Patient identifiers are stored separately to the unit’s research database which holds the HES extracts (including sensitive fields). ICL DFU researchers have no access to identifiable fields. Only two named data managers have access to the patient identifiable fields within the unit. The purpose of holding the patient identifiers for the last 3 years is to allow hospitals to further investigate any alerts around poor or good performance and to help improve the quality and safety of healthcare delivery. The HES extracts (including sensitive fields) are stored in the Research database where researchers are able to access the data to do their analyses. The HES extracts (including sensitive fields) are loaded on to the Research database with a unique identifier (fosid) being generated and added to the datasets. A new Extract_hesid for Dr Foster Intelligence Limited (DFI) is also generated using the SHA-256 hashing algorithm, compliant with the e-GIF Technical Standards Catalogue Version 6.2 based on the original Extract_hesid. An extract is taken from ICL DFU patient identifier server and copied to the server which is used to provide the Re-Identification service for the NHS Acute Trusts. Further data processing are carried out on the onward supply of data by DFI who have dedicated staff and processes as per below: • Linkage into spells and superspells, which can often span across financial years • HRG, Tariff and other PBR related fields, using the HRG Grouper software • Various clinical groupings, including CCS Diagnoses, Ambulatory Care Sensitive (ACS) conditions and Procedure Groups • Quality outcomes, including mortality, emergency readmission within 28 days, Long Length of stay and patient safety indicators • Patient-level predicted risks for these outcomes, based on national Logistic Regression models which are executed using R statistical software and updated monthly • Various other national benchmarks, including Length of stay percentiles and Standardised Admission Ratio benchmarks • Numerous efficiency-based metrics, including average length of stay, day case rate and potential bed days saved • Prescribed Specialised Services (PSS) groups, using the PSS Grouper software This process guarantees both DFI and ICL DFU are working from exactly the same data (both in terms of underlying patient linkage and derived fields), which is necessary for their joint projects. No record level data will be transferred outside of the EEA, either under this agreement or any related sub-licence. All persons accessing the data are substantive employees of Imperial Collage London.	1) Research into variations in quality of healthcare by provider: background to proposed work Imperial College London Dr Foster Unit (ICL DFU) work programme is designed to develop and validate indicators of quality and safety of healthcare, show variations in performance by unit and socio-demographic stratum and develop methods for risk prediction, risk adjustment and outlier detection. The unit’s work focuses on quality of care and patient safety, including healthcare-acquired infections (surgical wound infections and urinary tract infections) and safety indicators. Collaborative projects with clinical colleagues have helped develop and validate healthcare quality indicators other than mortality, including bariatric surgery, primary angioplasty rates, indicators for stroke care, obstetric care, orthopaedic redo rates and returns to theatre. ICL DFU is currently working on the following analyses: ‘Biggest bang per buck’ elements of treatment pathways for chronic diseases. By mapping out NHS hospital contacts and modelling the variation across units, the unit will determine the elements (e.g. readmissions, missed OPD appointments, surgery that could have been done as a day case) with the most potential for improvement. This forms part of the unit’s work with Imperial’s NIHR funded Patient Safety Translational Research Centre on the use of information for service improvement. (Dec 2017) Drivers of unscheduled return to theatre (or reoperation) in elective hip and knee replacements: correlation between Return To Theatre (RTT) and revision rates by surgeon; volume-outcome relation for RTT; risk of RTT following revision rates. The objective is to better understand these key metrics for the specialty: revision rates are of major interest to surgeons and are on the NHS Choices website. The unit has recently established that there is greater non-random variation in RTT rates between surgeons than between hospitals. (on-going) Predictors of readmissions and A&E attendance in patients with chronic diseases (heart failure, COPD, cancer). Readmissions are the focus of much attention worldwide in efforts to reduce costs and improve outcomes, but little is known about the role of A&E attendance (not ending in admission) in observed variations in readmission rates. The study has revealed that earlier OPD nonattendance is a strong risk factor for readmission. The objective is again to better understand readmissions as an indicator and to suggest reformulation if desirable. (Jun 2017) Travel time. Due to the well-documented relation between patient volume and outcomes, there is a growing drive to centralise certain services such as for stroke and elective surgery. Treatment rates for many conditions such as thoracic aortic disease (TAD) vary around the country. Using Lower Super Output Areas of the patient’s residence and the hospital postcode, researchers will first calculate how far patients currently travel for their TAD treatment and then the travel distance that would be incurred were surgical services retained only at large centres. The effect on outcomes will also be assessed. (Dec 2017) Modelling Health trajectories for Stroke patients ICL DFU is currently undertaking a study which involves the evaluation of patients who had a stroke and following them up for 5 years. The study involves people who had a stroke for the first time. Previous studies have been criticised for including patients with recurrent stroke. Based on previous research, ICL DFU has tracked back their chosen stroke patients for 10 years to ascertain whether the stroke event under observation was the first or recurrent. Moreover, ICL DFU has to evaluate important cardiovascular co-morbidities by looking at the patients hospital diagnosis made in the previous years. The study aims to identify stroke patients who are initially stable but later become high users of health care resources. ICL DFU also plans to look at pattern of causes of subsequent hospitalisation in the same cohort of patients. The study requires tracking back patients 10 years and following up for 5 years from the time of their index stroke event. (Jul 2018) Recent pressures on A&E and breaches of the 4-hour wait have led to concerns over pressure on A&E and inpatient capacity. ICL DFU intends to examine capacity measures for A&E and inpatient admissions, and the impact that pressure on resources might have on safety and patient outcomes with a view to better understanding key NHS metrics and patterns of service use to better match supply to need. (Dec 2016) ICL DFU is working in collaboration with the University of Manchester and supported by the Care Quality Commission, to improve understanding of the unit’s mortality alerts and to evaluate their impact as an intervention to reduce avoidable mortality within English NHS hospital trusts, focusing on two conditions commonly attributed to mortality alerts acute myocardial infarction and septicaemia. The aim of this study is to provide a descriptive analysis of all alerts, their relationships with other measures of quality and their impact on reducing avoidable mortality. (Dec 2016) International comparisons of service use and outcomes. England and the USA. The unit holds data from Centre for Medicare and Medicaid Services enrollees and from the Nationwide Inpatient Sample from the USA. Researchers have previously set out the methodological issues with using administrative data from multiple countries. This study will compare patient casemix, rates of outcomes such as infections and readmissions, and rates of surgery, for example in patients near the end of their life (overtreatment is a growing concern) between the two countries. The objective is to highlight areas of better or poorer performance by the NHS compared with the USA. ICL DFU has an extract of the Italian data and will be using HES data to compare hospital use for patients with heart failure in England compared with Italy. (on-going) Examples of key published research that have used HES data include: Palmer WL, Bottle A and Aylin P. Association between day of delivery and obstetric outcomes: observational study. BMJ 2015; 351: h5774. Bottle A, Goudie R, Cowie MR, Bell D, Aylin P, 2015, Relation between process measures and diagnosis-specific readmission rates in patients with heart failure, HEART, Vol: 101, Pages: 1704-1710, ISSN: 1355-6037 Aylin P; Alexandrescu R; Jen MH; Mayer EK; Bottle A. Day of week of procedure and 30-day mortality for elective surgery: retrospective analysis of hospital episode statistics. BMJ 2013;346:f2424. Palmer WL; Bottle A; Davie C; Vincent CA; Aylin P. Dying for the Weekend: A Retrospective Cohort Study on the Association Between Day of Hospital Presentation and the Quality and Safety of Stroke Care. Arch Neurol. 2012;69:1296-1303. Aylin P; Bottle A; Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007;334:1044. Aylin P, Yunus A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multicentre study. Qual Saf Health Care. 2010;19:213-217 Jen MH, Bottle A, Majeed A, Bell D, Aylin P. Early in-hospital mortality following trainee doctors' first day at work. PLoS One. 2009;4:e7103. For full publication list see unit website: http://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/unit_publications/ 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Dr Foster Intelligence Limited (DFI) is an independent healthcare information company. It provides a research grant to ICL DFU to develop indicators and methodologies to assist in the analysis of healthcare performance. ICL DFU works in collaboration with DFI to provide the NHS with a number of management information systems via the Dr Foster Analysis Toolkit. The main output created are benchmarked or standardised healthcare indicators & analysis such as mortality (SHMI/HSMR), LOS(Length of Stay), admission trends, readmission rates, patient safety indicators, referral patterns, market share analysis etc. As stated previously, outputs are to be used solely for the purposes of providing a management information function to the NHS. Outputs are provided via: • Dr Foster Analysis Toolkit – Use of Role Based Access to determine the level of data end users can see within the tool. • Value added services - Tabulations, Reports, Spreadsheets, Presentations, Articles & Projects. Outputs will be used by customers to investigate Clinical Quality, Performance and Business Development, specifically: • Assess and manage clinical quality and patient safety within NHS Organisations • Identify pathways where there is potential for improvement • Identify areas of best practice either within the Provider Trust or local/national health economies • Better understand how they compare to other Provider Trusts with similar case mixes • Identify improvements in operational efficiency • Understand patient outcomes • Identify and understand market activity • Monitor the impact of implemented changes • Identify variations in outcomes 3) Provision of a patient re-identification service for the NHS ICL DFU provides a patient re-identification service for the NHS which allows NHS provider trusts to investigate issues around quality and safety of care within their organisation, which have arisen out of performance alerts arising out of ICL DFU analyses (e.g. mortality alerts), or arising from DFI performance tools using ICL DFU methods. Authorised individuals within Provider Trusts are able to identify their own patients indicated in the DFI healthcare performance tools. From April 2015 to April 2016, there were over 3,600 successful logins from 75 NHS provider organisations. 64 provider trusts have used it more than 12 times per year (once a month) and one trust has used the re-identification service 425 times within this period. The re-identification service allows ICL DFU to supply NHS provider trusts with NHS Number and LOPATID using DFI healthcare performance tools without passing these identifiers on to DFI. No patient identifiers will ever be passed to DFI or any other organisation except the NHS provider trust from where the data originated. The patient identifiable data are kept separate to the anonymised and sensitive data. They are held on a different system to clinical data. All patient identifiable data are securely deleted on a rolling 3 year programme. The re-identification service is maintained by ICL DFU and is in full compliance of CAG approval reference:15/CAG/0005.	Imperial College London Dr Foster Unit (ICL DFU) works with the Care Quality Commission (CQC), contributing to its surveillance remit using the same methods and data. The unit generates monthly mortality alerts since 2007, based on high thresholds [1]. This was pivotal in alerting the then Healthcare Commission (HCC) to problems at the Mid Staffordshire NHS Foundation Trust between July and November 2007[2]. The resulting Public Inquiry recognised the role that the unit’s surveillance system of mortality alerts had to play in identifying Mid Staffs as an outlier [3]. Key recommendations, [4] reflecting the unit’s work, are that all healthcare provider organisations should develop and maintain systems which give effective real-time information on the performance of each of their services, specialist teams and consultants in relation to mortality, patient safety and minimum quality standards. A further recommendation is that summary hospital-level mortality indicators should be recognised as official statistics [5]. If ICL DFU is given continued access to the data, this monitoring tool that detected Mid Staffs will continue to monitor patient outcomes at acute hospitals and be ready to detect any future outliers. The unit will be able to assist the investigation of variations in outcomes at a local level by providing Local Patient ID, NHS Number and Consultant Code from the unit’s analyses to authorised users within trusts to enable reconciliation with local information systems and the instigation of clinical audits and case note reviews. ICL DFU mortality outlier outputs are used by CQC within their Hospital Inspection framework.(on-going) As a result of the unit’s leading role in the development of hospital mortality measures, in 2010 ICL DFU was invited to contribute to a DoH Commissioned expert panel (Steering Group for the National Review of the Hospital Standardised Mortality Ratio) [6] to develop a national indicator of hospital mortality. The resultant Summary-level Hospital Mortality Indicator (based in part on their HSMR methods) is now a public indicator used by all acute trusts. [7] Professor Sir Bruce Keogh suggests that a relatively “poor” SHMI should trigger further analysis or investigation by the hospital Board. The recent review (published in July 2013) into the quality of care and treatment provided by 14 hospital trusts with consistently high mortality in either measure led to 11 out of the 14 trusts identified being immediately placed on special measures. The review also informs the way in which hospital reviews and inspections are to be carried out with the recommendation that mortality is used as part of a broad set of triggers for conducting future inspections [8]. ICL DFU continues to advise the HSCIC on methodological issues around the Summary level Hospital Mortality Index (SHMI), and carry out analyses relating to this measure to assist in its development. (ongoing) The unit’s research on specific aspects of care has received a high media profile and has been highly cited. Their research on weekend mortality in emergency care, analysis of mortality associated with the junior doctor changeover and work on elective procedures and mortality by day of the week resulted in front page broad sheet coverage, and radio and TV interviews. (ongoing) https://www1.imperial.ac.uk/publichealth/departments/pcph/research/drfosters/inthemedia/ The unit’s “Out of hours” work has been a key driver in moving NHS towards 7/7 care. Headlines include, “NHS Services – open seven days a week: every day counts” and, “Sunday Times Safe Weekend Care”. As a result of the unit’s published research into the junior doctor changeover, Bruce Keogh introduced a week's shadowing where newly qualified doctors worked alongside more senior ones for a week before they start work in August. The Academy of Medical Royal Colleges published proposals (16th April 2014) suggesting all Foundation Year 1 posts should begin on the first Wednesday in August as has always been the case, but other training posts should begin in September.(on-going) As part of the ‘biggest bang per buck’ analysis, econometric modelling will suggest which elements of the patient pathway are the most costly. Combining this with modelling of variation by unit will suggest priorities for improvement. Outputs will benefit managers, commissioners and patients. (Dec 2017) Analyses of return to theatre and joint revision for elective hip and knee surgery will help orthopaedic surgeons, commissioners and patients understand these key quality markers for this specialty and devise appropriate improvement projects, for instance by determining which patients are at the highest risk and therefore need more rigorous follow-up. (on-going) ICL DFU intends to examine demand and capacity measures for A&E and admissions, and the impact that pressure on resources might have on safety and patient outcomes. By profiling hospital trusts in terms of demand, patient mix and outcomes, researchers will better understand key NHS metrics and patterns of service use and thereby help managers manage demand. (Jun 2017) Regarding the travel time analysis, using Lower Super Output Areas would enable us to study the effect of distance from home to hospital on patient outcomes. This also allows geographical access to services to be estimated, as researchers can calculate how far patients must travel for their treatment both now and after any future service reorganisation. (Dec 2017) ICL DFU analysis of their mortality alerting system will allow us to improve the alerting process and provide a better indication of how hospitals should investigate them to reduce mortality (including what are the key contributing factors to the alerts and to subsequent improvement in mortality by the hospitals). (Dec 2016) The modelling of health trajectories in stroke patients will improve risk stratification and understanding of the medium-term prognosis and needs. This will also allow better econometric modelling of NHS service use. (Jul 2018) References [1] CQC Quarterly publication of individual outlier alerts for high mortality: Explanatory text (URL available at http://www.cqc.org.uk/public/about-us/monitoring-mortality-trends) [2] Investigation into Mid Staffordshire NHS Foundation trust. Healthcare Commission 2009. Outcomes for patients and mortality rates. Pages 20 - 25 http://www.midstaffspublicinquiry.com/sites/default/files/Healthcare_Commission_report_on_Mid_Staffs.pdf [3] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Volume 1. Pages 458 - 466 http://www.midstaffspublicinquiry.com/report. [4] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 262: http://www.midstaffspublicinquiry.com/report). [5] Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry 2013. Executive Summary. Recommendation 271: http://www.midstaffspublicinquiry.com/report. [6] Development of the new Summary Hospital-level Mortality Indicator. Department of Health Website. http://www.dh.gov.uk/health/2011/10/shmi-update/ [7] Indicator Specification: Summary Hospital-level Mortality Indicator. http://www.ic.nhs.uk/SHMI [8] Review into the quality of care and treatment provided by 14 hospital trusts in England: overview report Professor Sir Bruce Keogh KBE. http://www.nhs.uk/NHSEngland/bruce-keogh-review/Documents/outcomes/keogh-review-final-report.pdf 2) Support the provision of a management information systems (Dr Foster Analysis Toolkit) for the NHS Expected benefits include: • Enabling NHS acute trusts to measure, compare and benchmark key quality indicator trends – focusing on risk-adjusted measures of mortality, readmissions and length of stay in hospital. • Providing evidence to instigate clinical audit and investigations related to quality of care, such as highlighting potential poor clinical coding or quality/efficiency concerns. • Validating other mortality indicators – such as HSMR, Custom alerts and crude mortality. • Enabling NHS acute trusts and commissioners to use performance information to identify, quantify and act on opportunities to improve efficiency of health services. • Understanding areas of best practice amongst our customers and facilitate interactions with other customers who are not performing as well to support quality and efficiency improvement. • Helping clinicians and managers by providing independent and authoritative analysis of the variations that exist in acute hospital care in a way that is meaningful for them and that is understandable to patients and the public. • Highlighting topics of interest to the health industry and wider public to enable discussion and improvement in healthcare provision. • Publication of articles around variations of healthcare within the NHS is in the public interest and supports the government agenda for transparency by promoting choice and accountability within the NHS. • Maintaining the focus of the organisations on improvement. • Raising public and professional awareness through the Dr Foster's Hospital Guide regarding issues that affect the quality and efficiency of care provided by the NHS by publishing new information about variation in outcomes at the level of individual hospitals. In recent years, the guide has focussed on issues of clinical and managerial concern such as weekend care, overcrowding, management of chronic conditions and variations in access to elective care. In each case, the approach has been to identify effects that are known from the academic literature and to show their impact here and now in English NHS hospitals. By publishing this information Dr Foster Limited support the improvement of healthcare in England. How will these benefits be measured: Benefits are ongoing as the outputs described above are used within NHS Trusts’ internal monthly reporting and quality processes. Dr Foster Intelligence Ltd (DFI) services allow performance of NHS Provider Trusts to be monitored and trended over time and therefore provide customers with the ability to measure changes in quality and performance particularly in instances where customers have been alerted and they have worked with them to understand the causes of worse than expected performance. DFI intends to provide an online customer survey within the Dr Foster Analytics Tool to capture customer feedback and associated benefits, this data will form the foundation for improving their services and enable them to provide HSCIC, and other relevant bodies, with tangible evidence to support their ongoing use of HES data. DFI welcomes the opportunity to work with HSCIC to ensure information captured can support their ongoing supply and use of HES data. When will these be achieved: As a majority of benefits are achieved on an ongoing basis, it is not possible to outline a specific target date for achievement of the benefits outlined as they are reliant on a range of factors outside of ICL DFU and DFI’s control. However, whenever there are areas of particular concern about performance against key indicators, the 2 parties act immediately to alert relevant stakeholders and offer their assistance in better understanding and addressing them.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	IMS Health is a brand comprised of a number of legal entities which provide technology and services to healthcare. This application/agreement is a request for pseudonymised record-level HES data which will be controlled by two legal entities: • IMS Health TS • IMS Health UK ltd Hereafter, these two entities will be referred to collectively as IMS Health. IMS Health will use the HES data to perform two types of service: 1. Data visualisation and benchmarking tools which includes: i) Care Pathway Analyser (formerly visualise treatment pathways) ii) Hospital Feedback services iii) Visualise Healthcare Data 2) Advanced Statistical Analysis (formerly referred to as structured disease analysis) 1) The data visualisation and benchmarking tools are described below: • Care Pathway Analyser (CPA). Presents users with simple views of aggregated care pathways. This allows investigation of the causes of variation in patient pathways and the subsequent impact on service delivery. • Hospital Feedback Services (HFS). A dashboard allowing chief pharmacists to optimise their use of medicines. It will also allow them to monitor their own performance against internal targets and benchmark against similar hospitals. This service is still in development. NHS Trusts will be granted access to HFS in exchange for continued supply to non-identifiable prescription data and agreement that IMS Health Ltd can use the data for more further research. • Visualise Healthcare Data (VHD). A suite of tools/reports that allows users to perform queries on aggregated HES data then view graphs and tables. 2) Advanced Statistical Analysis includes: diagnostic algorithm development, epidemiology, health economics and outcomes research studies. Both services will only be provided to the following categories of types of organisation: - Providers of healthcare services • Clinical Commissioning Groups • Commissioning Support Units (CSU’s) • Hospital Trusts • Private secondary care providers • Mental Health trusts • Community Provider Trusts • Pharmacies • NHS England • Public Health England • Health and Wellbeing Boards - Universities - Life science industry • Pharmaceutical companies • Medical Device companies • Industry bodies – limited to the Association of the British Pharmaceutical Industry (ABPI), Ethical Medicines Industry Group (EMIG) and the Proprietary Associated of Great Britain (PAGB) Third parties will only see aggregated and small number suppressed data. The number of organisations to whom IMS Health provide products and services changes regularly. In the year to date IMS Health have worked on 31 Advanced Statistical Analysis projects and Data Visualisation and Benchmarking services using HES data held under this DSA. Of these projects, approximately half were for repeat customers (who had purchased at least one other tool or project from IMS Health within that period). When finalised, HFS will be given to all NHS Trusts. IMS Health understands the importance of data minimisation and outline IMS Health’s requirement for national, timely HES data in the following paragraphs. IMS Health requires national data to enable the end users of IMS Health’s tools to benchmark against organisations in their local area or with similar demographic characteristics. IMS Health also requires national data to inform economic analyses for inclusion in submissions to NICE, which makes decisions at a national level. HFS is intended for all chief pharmacists in NHS Trusts. The requirement for timely data is because the commissioners and providers to whom IMS Health provide IMS Health’s tools need to make decisions based on the most up-to-date information. IMS Health won an open tender to perform a medicines optimisation study for a group of cancer treatment providers. More detail on this project is given in later sections. Historic data is required to support Advanced Statistical Analysis projects, as historical data allows robust analysis of trends over time.	IMS Health will receive the data from HSCIC and will apply derivations. No linkage is carried out to other datasets. In the context of this application/agreement applying derivations does not mean linking to other patient-level information. In this application/agreement, applying derivations means that IMS Health will use non-identifiable data to derive new information. For example, length of stay is approximated using the relationship between admission and discharge dates and the cost of an admission is approximated using the NHS payment by results tariff. For data visualisation and benchmarking services, further derivations are applied to allow benchmarking and the data is presented in dashboards alongside other IMS Health and publically available data sources e.g. Quality Outcomes Framework data. All data visualisation and benchmarking tools are hosted by IMS Health. All data seen by end users is aggregated, small number are suppressed and are compliant with the HES Analysis Guide. Usage of these tools is auditable and role based access controls are applied. Customers using these tools are contractually prevented from using the data for solely commercial purposes. For advanced scientific analysis, IMS Health produce bespoke analysis for external organisations on a project by project basis. All requests for bespoke analysis are subject to review by an independent scientific advisory committee (ISEAC – details in the following paragraph) who review the proposed study design. If ISEAC approves the study, it is logged on an access control register and the IMS Health researchers are allowed to access the relevant subset of HES data. The researchers will present the results of their analysis to external organisations in the form of aggregated, small number suppressed tables compliant with the HES Analysis Guide. These outputs may also take the form of counts, proportions or formulae. Anonymised abstracts will be published on the IMS Health global bibliography 6-12 months after completion of the study. ISEAC is a group of medical and scientific advisors who are independent of IMS Health. For studies based on HES data held under this Data Sharing Agreement the role of this committee is to ensure that any study performed is complaint with this data sharing agreement and by extension the Care Act 2014. All ISEAC decisions are binding, and any studies not approved will not be performed unless revised and subsequently approved. ISEAC records of decisions can be made available to NHS Digital under the caveat that they will remain commercial in confidence.	For clarity services covered within this application only produce two types of output: • Dashboards • Aggregated tables In both cases outputs are aggregated and small numbers suppressed in line with the HES Analysis Guide. Details for each of the services are given below. Visualise Healthcare Data (VHD): VHD is an internet browser based application, an iPad application or a bespoke report. Users are given role based access to the applications. The applications allow users to produce graphical and tabular estimates of burden of disease, cost of care, common comorbidities and similar analyses. These analyses may be stratified by diagnosis, organisation and other similar parameters. Care Pathway Analyser (CPA): CPA is currently an internet browser based application and other delivery methods are in development. CPA will either be deployed directly to users or used to support consulting projects. In the former users are given role based access to the application which will allow them to analyse images of aggregated pathways. In the latter, outputs will be presentations and reports containing pathway images as well as IMS Health recommendations. Hospital Feedback Services (HFS): Version 1.0 of the HFS tool has been presented to the NHS as of December 2016 (as a browser-based application) and other delivery methods are in development. Chief pharmacists will be given role-based access to a dashboard which will show them aggregated HES data, aggregated prescribing data and performance indicators. These data will be presented in graphs and tables. It is expected that log in details for Version 1.0 will be supplied by the end of Q1 2017. Advanced Statistical Analysis The data included in advanced statistical analysis are always aggregated and small number suppressed in line with the HES Analysis Guide. These outputs are produced to meet different objectives and delivered in different ways. A health economic analysis may require analysis of the data to estimate the cost of managing a given condition then used as an input in an economic model for a NICE submission. Developing a diagnostic algorithm will result in the production of a formula which may be presented to clinicians in an Excel based calculator with an explanatory report or presentation. Many of the outputs of advanced statistical analysis are reported in journal articles or conference presentations.	IMS operates on a project by project basis. Each project using this data source must generate benefit to healthcare, for example by: • Providing detailed evidence based recommendations for how to improve care in specific organisations or therapy areas • Giving healthcare professionals (HCPs) the ability to understand their own organisation’s performance via dashboards and reports; enabling them to reduce cost whilst delivering best practice care • Providing analyses to decision making bodies such as the European Medicines Agency and the National Institute for Health and Care Excellence; in order to enable them to grant patients access to innovative medicines • Contributing to knowledge to the medical community in order to stimulate further research into improving patient care Examples of how previous projects have provided benefit to patient care are given below. Developing diagnostic pathways in Fabry Disease: IMS Health developed a diagnostic algorithm for patients with Fabry disease. In current ICD-10 coding the 4 characters code (E75.2 other sphingolipidosis) encompasses 5 different diseases: Gocher disease, Krabbe disease, Niemann-Pick disease and Metachromatic leukodystrophy. Despite the similarities in disease genesis the symptoms, treatment pathways, procedures and prognosis are different. By identifying the actual underlying disease patient would be put on the correct treatment pathway more quickly and better managed their condition. The project involved working with Lysosomal storage disorder (LSD) clinical experts to understand the different diseases, the epidemiology and the diagnosis and treatment pathway. Clinicians then worked with IMS to identify inclusion and exclusion criteria for Fabry disease based on the specialties visited by the patient, associated diagnosis codes (ICD-10 codes), procedures and treatments performed (OPCS codes), and LSD specialty centres visited (key specialist centres). The team also divided some of the variables by age of the patients to define patients for disease that typically affect certain age groups. The output was a logic-based algorithm which could be used to identify Fabry disease patients in routine clinical practice. This project was completed in March 2016, the expected benefit is an improvement in the speed and accuracy of Fabry disease diagnoses. Analysis and validation of musculoskeletal services for the NHS and Care UK: Working with Aylesbury Vale CCG, Chiltern CCG, Buckinghamshire NHS Trust & Care UK, IMS Health modelled the level of service in changing environments over the next five-year period in order to improve their long-term planning process. The analysis used HES data plus data supplied from 8 CCGs. The IMS Health team designed interventions to make the service more efficient and compiled forecasts to show the impact of these interventions on the forecast. The analysis was initially summarised in a presentation but subsequently delivered as dashboard that allowed the clients to model and understand the impact of pursuing different strategies for transformation and therefore inform decision making. For example, the model predicted that reducing the rate of inpatient spells with excess bed days had a low impact on overall MSK spend; however, reducing the rate of inpatient spells where the patient had complications or comorbidities or moving outpatient appointments to the community had a much greater potential to increase efficiency. The research proposal for this project was submitted in October 2014. The final analysis was delivered in February 2016. The expected benefit is that this tool will allow HCPs to understand how to deliver more effective cost saving programmes. Patient profiling and pathway analysis for University Hospital South Manchester: In response to a requirement from a senior clinician at the University Hospital of South Manchester (UHMS), IMS performed an exploratory analysis using VHD in pneumonia and cellulitis. In both diseases, the analysis showed that more than half of all admissions were in patients from the most deprived 20% of neighbourhoods. The analysis went on to benchmark UHSM against its peers and found it had the third highest readmissions ratio in the region. The UHSM project also included analysis of patient pathways. The analysis found that the average pneumonia pathway was 69% longer than the national average and cost the NHS 37% more. Further analysis showed 38% of pneumonia pathways in Manchester contained at least one COPD-related event; this was 10 percentage points more than the national average. On average, this group of pathways was more expensive and longer than the group without a recorded COPD event. The results of this work were presented to the Trust in February 2016. The Trust expects to reduce costs and improve patient outcomes by applying best practice from Trusts with a similar case mix. Analysis of cardiology pathways for the Heart of England NHS Foundation Trust: CPA and VHD were used to review the cardiology services in the Heart of England Foundation Trust. The analysis, combined with the Trust’s own data, aimed to improve the efficiency of care. The analysis was presented at various stages to a team from the Trust in early 2016. Following on from the analysis, IMS Health recommended providing care based on clusters of procedures as this would allow the Trust to monitor consistency more closely and improve demand forecasting. IMS Health expects that this analysis will allow the Trust to improve care by being better prepared for demand for cardiology services. Using CPA to streamline hip replacement pathways in Cambridge and Peterborough CCG: CPA in combination with HES and the CCG’s own local activity data was used to establish a gold standard pathway in Cambridgeshire and Peterborough for four providers in the region. Treatment pathway analysis and benchmarking against similar CCGs enabled them to envisage where the NHS’s Cost Improvement Programme (CIP) and the Quality, Innovation Productivity and Prevention (QIPP) programme could be delivered. In the words of the Local Chief Officer “IMS Health provided me the insight to see where CIP and QIPP could be delivered by commissioning shorter pathways in line with best practice” This work was presented in February 2016. The expected benefit is that this analysis will help HCPs deliver hip replacements safely and efficiently in line with best practice. Three further examples of projects under development and their expected benefits are: Cancer Vanguard Medicines Optimisation Project: IMS Health has won a tender with a group of NHS Trusts. The aim of the project is to optimise the use of cancer medicines and reduce the unnecessary variation in cancer care and is currently in the scoping phase. IMS Health will use Advanced Statistical Analysis and the Care Pathway Analyser tool to deliver this project; it will involve a review of medicines usage in cancer, and identification of avoidable variation. The output will be a model to reduce the cost of treating cancer to ensure clarity around best practice processes. Patient reported outcomes will also ensure that the relationship between best practice and improvement in patients’ quality of life is quantified. This analysis is being developed alongside HCPs and the expected benefit is that the results will be presented back to HCPs in a way that will allow them to improve patients’ quality of life in a cost effective manner. More information about the Cancer Vanguard can be found here - http://cancervanguard.nhs.uk/about/. Staffordshire CCGs and the Rightcare programme: IMS Health is working with the Director of Strategic Programmes for a group of CCGs including: Cannock and Chase CCG, Stafford and Surrounds CCG, South East Staffs and Seisdon Peninsula CCG. The Director would like to use CPA to support the implementation of the NHS Rightcare programme. The Director had the following to say about the initiative: “NHS Rightcare (http://www.rightcare.nhs.uk/) promotes the principle of eliminating unwarranted variation in healthcare. The IMS care pathway analysis tool allows commissioners and providers to see variation in care provided and benchmark compliance with best practice utilising national HES (Hospital Episode Statistics) data in conjunction with locally available data. It is therefore a potentially valuable tool in allowing commissioners and providers to redesign pathways to achieve high quality affordable care.” Hospital Feedback Services: IMS Health is committed to ensuring that the NHS chief pharmacists have accurate and up-to-date information in order to better manage their drug dispensing. The IMS medicines optimisation dashboard is designed to include IMS Health’s Hospital Pharmacy Audit data and National HES data. The ability to include HES data is a vital component in ensuring that the output accurately reflects the seasonal variation in hospital activity and medicines usage. For example, the antibiotic usage dashboard includes the following report: Ratio of Defined Daily Dose of all dispensed antibacterial (ATC J1) products per 1000 admissions. It is the HES data that ensures accuracy on the 1000 admissions and enables IMS Health to account for seasonal variation in the analysis. IMS Health will provide HFS to all hospital trusts. It is being designed in collaboration with the chief pharmacist community to ensure that it meets their needs. IMS Health expects HFS to benefit healthcare by allowing chief pharmacist to improve prescribing efficiency leading to a financial savings; it will also allow them to better forecast the amount of medicines required and therefore prevent waste:
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	IMS Health is a brand comprised of a number of legal entities which provide technology and services to healthcare. This application/agreement is a request for pseudonymised record-level HES data which will be controlled by two legal entities: • IMS Health TS • IMS Health UK ltd Hereafter, these two entities will be referred to collectively as IMS Health. IMS Health will use the HES data to perform two types of service: 1. Data visualisation and benchmarking tools which includes: i) Care Pathway Analyser (formerly visualise treatment pathways) ii) Hospital Feedback services iii) Visualise Healthcare Data 2) Advanced Statistical Analysis (formerly referred to as structured disease analysis) 1) The data visualisation and benchmarking tools are described below: • Care Pathway Analyser (CPA). Presents users with simple views of aggregated care pathways. This allows investigation of the causes of variation in patient pathways and the subsequent impact on service delivery. • Hospital Feedback Services (HFS). A dashboard allowing chief pharmacists to optimise their use of medicines. It will also allow them to monitor their own performance against internal targets and benchmark against similar hospitals. This service is still in development. NHS Trusts will be granted access to HFS in exchange for continued supply to non-identifiable prescription data and agreement that IMS Health Ltd can use the data for more further research. • Visualise Healthcare Data (VHD). A suite of tools/reports that allows users to perform queries on aggregated HES data then view graphs and tables. 2) Advanced Statistical Analysis includes: diagnostic algorithm development, epidemiology, health economics and outcomes research studies. Both services will only be provided to the following categories of types of organisation: - Providers of healthcare services • Clinical Commissioning Groups • Commissioning Support Units (CSU’s) • Hospital Trusts • Private secondary care providers • Mental Health trusts • Community Provider Trusts • Pharmacies • NHS England • Public Health England • Health and Wellbeing Boards - Universities - Life science industry • Pharmaceutical companies • Medical Device companies • Industry bodies – limited to the Association of the British Pharmaceutical Industry (ABPI), Ethical Medicines Industry Group (EMIG) and the Proprietary Associated of Great Britain (PAGB) Third parties will only see aggregated and small number suppressed data. The number of organisations to whom IMS Health provide products and services changes regularly. In the year to date IMS Health have worked on 31 Advanced Statistical Analysis projects and Data Visualisation and Benchmarking services using HES data held under this DSA. Of these projects, approximately half were for repeat customers (who had purchased at least one other tool or project from IMS Health within that period). When finalised, HFS will be given to all NHS Trusts. IMS Health understands the importance of data minimisation and outline IMS Health’s requirement for national, timely HES data in the following paragraphs. IMS Health requires national data to enable the end users of IMS Health’s tools to benchmark against organisations in their local area or with similar demographic characteristics. IMS Health also requires national data to inform economic analyses for inclusion in submissions to NICE, which makes decisions at a national level. HFS is intended for all chief pharmacists in NHS Trusts. The requirement for timely data is because the commissioners and providers to whom IMS Health provide IMS Health’s tools need to make decisions based on the most up-to-date information. IMS Health won an open tender to perform a medicines optimisation study for a group of cancer treatment providers. More detail on this project is given in later sections. Historic data is required to support Advanced Statistical Analysis projects, as historical data allows robust analysis of trends over time.	IMS Health will receive the data from HSCIC and will apply derivations. No linkage is carried out to other datasets. In the context of this application/agreement applying derivations does not mean linking to other patient-level information. In this application/agreement, applying derivations means that IMS Health will use non-identifiable data to derive new information. For example, length of stay is approximated using the relationship between admission and discharge dates and the cost of an admission is approximated using the NHS payment by results tariff. For data visualisation and benchmarking services, further derivations are applied to allow benchmarking and the data is presented in dashboards alongside other IMS Health and publically available data sources e.g. Quality Outcomes Framework data. All data visualisation and benchmarking tools are hosted by IMS Health. All data seen by end users is aggregated, small number are suppressed and are compliant with the HES Analysis Guide. Usage of these tools is auditable and role based access controls are applied. Customers using these tools are contractually prevented from using the data for solely commercial purposes. For advanced scientific analysis, IMS Health produce bespoke analysis for external organisations on a project by project basis. All requests for bespoke analysis are subject to review by an independent scientific advisory committee (ISEAC – details in the following paragraph) who review the proposed study design. If ISEAC approves the study, it is logged on an access control register and the IMS Health researchers are allowed to access the relevant subset of HES data. The researchers will present the results of their analysis to external organisations in the form of aggregated, small number suppressed tables compliant with the HES Analysis Guide. These outputs may also take the form of counts, proportions or formulae. Anonymised abstracts will be published on the IMS Health global bibliography 6-12 months after completion of the study. ISEAC is a group of medical and scientific advisors who are independent of IMS Health. For studies based on HES data held under this Data Sharing Agreement the role of this committee is to ensure that any study performed is complaint with this data sharing agreement and by extension the Care Act 2014. All ISEAC decisions are binding, and any studies not approved will not be performed unless revised and subsequently approved. ISEAC records of decisions can be made available to NHS Digital under the caveat that they will remain commercial in confidence.	For clarity services covered within this application only produce two types of output: • Dashboards • Aggregated tables In both cases outputs are aggregated and small numbers suppressed in line with the HES Analysis Guide. Details for each of the services are given below. Visualise Healthcare Data (VHD): VHD is an internet browser based application, an iPad application or a bespoke report. Users are given role based access to the applications. The applications allow users to produce graphical and tabular estimates of burden of disease, cost of care, common comorbidities and similar analyses. These analyses may be stratified by diagnosis, organisation and other similar parameters. Care Pathway Analyser (CPA): CPA is currently an internet browser based application and other delivery methods are in development. CPA will either be deployed directly to users or used to support consulting projects. In the former users are given role based access to the application which will allow them to analyse images of aggregated pathways. In the latter, outputs will be presentations and reports containing pathway images as well as IMS Health recommendations. Hospital Feedback Services (HFS): Version 1.0 of the HFS tool has been presented to the NHS as of December 2016 (as a browser-based application) and other delivery methods are in development. Chief pharmacists will be given role-based access to a dashboard which will show them aggregated HES data, aggregated prescribing data and performance indicators. These data will be presented in graphs and tables. It is expected that log in details for Version 1.0 will be supplied by the end of Q1 2017. Advanced Statistical Analysis The data included in advanced statistical analysis are always aggregated and small number suppressed in line with the HES Analysis Guide. These outputs are produced to meet different objectives and delivered in different ways. A health economic analysis may require analysis of the data to estimate the cost of managing a given condition then used as an input in an economic model for a NICE submission. Developing a diagnostic algorithm will result in the production of a formula which may be presented to clinicians in an Excel based calculator with an explanatory report or presentation. Many of the outputs of advanced statistical analysis are reported in journal articles or conference presentations.	IMS operates on a project by project basis. Each project using this data source must generate benefit to healthcare, for example by: • Providing detailed evidence based recommendations for how to improve care in specific organisations or therapy areas • Giving healthcare professionals (HCPs) the ability to understand their own organisation’s performance via dashboards and reports; enabling them to reduce cost whilst delivering best practice care • Providing analyses to decision making bodies such as the European Medicines Agency and the National Institute for Health and Care Excellence; in order to enable them to grant patients access to innovative medicines • Contributing to knowledge to the medical community in order to stimulate further research into improving patient care Examples of how previous projects have provided benefit to patient care are given below. Developing diagnostic pathways in Fabry Disease: IMS Health developed a diagnostic algorithm for patients with Fabry disease. In current ICD-10 coding the 4 characters code (E75.2 other sphingolipidosis) encompasses 5 different diseases: Gocher disease, Krabbe disease, Niemann-Pick disease and Metachromatic leukodystrophy. Despite the similarities in disease genesis the symptoms, treatment pathways, procedures and prognosis are different. By identifying the actual underlying disease patient would be put on the correct treatment pathway more quickly and better managed their condition. The project involved working with Lysosomal storage disorder (LSD) clinical experts to understand the different diseases, the epidemiology and the diagnosis and treatment pathway. Clinicians then worked with IMS to identify inclusion and exclusion criteria for Fabry disease based on the specialties visited by the patient, associated diagnosis codes (ICD-10 codes), procedures and treatments performed (OPCS codes), and LSD specialty centres visited (key specialist centres). The team also divided some of the variables by age of the patients to define patients for disease that typically affect certain age groups. The output was a logic-based algorithm which could be used to identify Fabry disease patients in routine clinical practice. This project was completed in March 2016, the expected benefit is an improvement in the speed and accuracy of Fabry disease diagnoses. Analysis and validation of musculoskeletal services for the NHS and Care UK: Working with Aylesbury Vale CCG, Chiltern CCG, Buckinghamshire NHS Trust & Care UK, IMS Health modelled the level of service in changing environments over the next five-year period in order to improve their long-term planning process. The analysis used HES data plus data supplied from 8 CCGs. The IMS Health team designed interventions to make the service more efficient and compiled forecasts to show the impact of these interventions on the forecast. The analysis was initially summarised in a presentation but subsequently delivered as dashboard that allowed the clients to model and understand the impact of pursuing different strategies for transformation and therefore inform decision making. For example, the model predicted that reducing the rate of inpatient spells with excess bed days had a low impact on overall MSK spend; however, reducing the rate of inpatient spells where the patient had complications or comorbidities or moving outpatient appointments to the community had a much greater potential to increase efficiency. The research proposal for this project was submitted in October 2014. The final analysis was delivered in February 2016. The expected benefit is that this tool will allow HCPs to understand how to deliver more effective cost saving programmes. Patient profiling and pathway analysis for University Hospital South Manchester: In response to a requirement from a senior clinician at the University Hospital of South Manchester (UHMS), IMS performed an exploratory analysis using VHD in pneumonia and cellulitis. In both diseases, the analysis showed that more than half of all admissions were in patients from the most deprived 20% of neighbourhoods. The analysis went on to benchmark UHSM against its peers and found it had the third highest readmissions ratio in the region. The UHSM project also included analysis of patient pathways. The analysis found that the average pneumonia pathway was 69% longer than the national average and cost the NHS 37% more. Further analysis showed 38% of pneumonia pathways in Manchester contained at least one COPD-related event; this was 10 percentage points more than the national average. On average, this group of pathways was more expensive and longer than the group without a recorded COPD event. The results of this work were presented to the Trust in February 2016. The Trust expects to reduce costs and improve patient outcomes by applying best practice from Trusts with a similar case mix. Analysis of cardiology pathways for the Heart of England NHS Foundation Trust: CPA and VHD were used to review the cardiology services in the Heart of England Foundation Trust. The analysis, combined with the Trust’s own data, aimed to improve the efficiency of care. The analysis was presented at various stages to a team from the Trust in early 2016. Following on from the analysis, IMS Health recommended providing care based on clusters of procedures as this would allow the Trust to monitor consistency more closely and improve demand forecasting. IMS Health expects that this analysis will allow the Trust to improve care by being better prepared for demand for cardiology services. Using CPA to streamline hip replacement pathways in Cambridge and Peterborough CCG: CPA in combination with HES and the CCG’s own local activity data was used to establish a gold standard pathway in Cambridgeshire and Peterborough for four providers in the region. Treatment pathway analysis and benchmarking against similar CCGs enabled them to envisage where the NHS’s Cost Improvement Programme (CIP) and the Quality, Innovation Productivity and Prevention (QIPP) programme could be delivered. In the words of the Local Chief Officer “IMS Health provided me the insight to see where CIP and QIPP could be delivered by commissioning shorter pathways in line with best practice” This work was presented in February 2016. The expected benefit is that this analysis will help HCPs deliver hip replacements safely and efficiently in line with best practice. Three further examples of projects under development and their expected benefits are: Cancer Vanguard Medicines Optimisation Project: IMS Health has won a tender with a group of NHS Trusts. The aim of the project is to optimise the use of cancer medicines and reduce the unnecessary variation in cancer care and is currently in the scoping phase. IMS Health will use Advanced Statistical Analysis and the Care Pathway Analyser tool to deliver this project; it will involve a review of medicines usage in cancer, and identification of avoidable variation. The output will be a model to reduce the cost of treating cancer to ensure clarity around best practice processes. Patient reported outcomes will also ensure that the relationship between best practice and improvement in patients’ quality of life is quantified. This analysis is being developed alongside HCPs and the expected benefit is that the results will be presented back to HCPs in a way that will allow them to improve patients’ quality of life in a cost effective manner. More information about the Cancer Vanguard can be found here - http://cancervanguard.nhs.uk/about/. Staffordshire CCGs and the Rightcare programme: IMS Health is working with the Director of Strategic Programmes for a group of CCGs including: Cannock and Chase CCG, Stafford and Surrounds CCG, South East Staffs and Seisdon Peninsula CCG. The Director would like to use CPA to support the implementation of the NHS Rightcare programme. The Director had the following to say about the initiative: “NHS Rightcare (http://www.rightcare.nhs.uk/) promotes the principle of eliminating unwarranted variation in healthcare. The IMS care pathway analysis tool allows commissioners and providers to see variation in care provided and benchmark compliance with best practice utilising national HES (Hospital Episode Statistics) data in conjunction with locally available data. It is therefore a potentially valuable tool in allowing commissioners and providers to redesign pathways to achieve high quality affordable care.” Hospital Feedback Services: IMS Health is committed to ensuring that the NHS chief pharmacists have accurate and up-to-date information in order to better manage their drug dispensing. The IMS medicines optimisation dashboard is designed to include IMS Health’s Hospital Pharmacy Audit data and National HES data. The ability to include HES data is a vital component in ensuring that the output accurately reflects the seasonal variation in hospital activity and medicines usage. For example, the antibiotic usage dashboard includes the following report: Ratio of Defined Daily Dose of all dispensed antibacterial (ATC J1) products per 1000 admissions. It is the HES data that ensures accuracy on the 1000 admissions and enables IMS Health to account for seasonal variation in the analysis. IMS Health will provide HFS to all hospital trusts. It is being designed in collaboration with the chief pharmacist community to ensure that it meets their needs. IMS Health expects HFS to benefit healthcare by allowing chief pharmacist to improve prescribing efficiency leading to a financial savings; it will also allow them to better forecast the amount of medicines required and therefore prevent waste:
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	IMS Health is a brand comprised of a number of legal entities which provide technology and services to healthcare. This application/agreement is a request for pseudonymised record-level HES data which will be controlled by two legal entities: • IMS Health TS • IMS Health UK ltd Hereafter, these two entities will be referred to collectively as IMS Health. IMS Health will use the HES data to perform two types of service: 1. Data visualisation and benchmarking tools which includes: i) Care Pathway Analyser (formerly visualise treatment pathways) ii) Hospital Feedback services iii) Visualise Healthcare Data 2) Advanced Statistical Analysis (formerly referred to as structured disease analysis) 1) The data visualisation and benchmarking tools are described below: • Care Pathway Analyser (CPA). Presents users with simple views of aggregated care pathways. This allows investigation of the causes of variation in patient pathways and the subsequent impact on service delivery. • Hospital Feedback Services (HFS). A dashboard allowing chief pharmacists to optimise their use of medicines. It will also allow them to monitor their own performance against internal targets and benchmark against similar hospitals. This service is still in development. NHS Trusts will be granted access to HFS in exchange for continued supply to non-identifiable prescription data and agreement that IMS Health Ltd can use the data for more further research. • Visualise Healthcare Data (VHD). A suite of tools/reports that allows users to perform queries on aggregated HES data then view graphs and tables. 2) Advanced Statistical Analysis includes: diagnostic algorithm development, epidemiology, health economics and outcomes research studies. Both services will only be provided to the following categories of types of organisation: - Providers of healthcare services • Clinical Commissioning Groups • Commissioning Support Units (CSU’s) • Hospital Trusts • Private secondary care providers • Mental Health trusts • Community Provider Trusts • Pharmacies • NHS England • Public Health England • Health and Wellbeing Boards - Universities - Life science industry • Pharmaceutical companies • Medical Device companies • Industry bodies – limited to the Association of the British Pharmaceutical Industry (ABPI), Ethical Medicines Industry Group (EMIG) and the Proprietary Associated of Great Britain (PAGB) Third parties will only see aggregated and small number suppressed data. The number of organisations to whom IMS Health provide products and services changes regularly. In the year to date IMS Health have worked on 31 Advanced Statistical Analysis projects and Data Visualisation and Benchmarking services using HES data held under this DSA. Of these projects, approximately half were for repeat customers (who had purchased at least one other tool or project from IMS Health within that period). When finalised, HFS will be given to all NHS Trusts. IMS Health understands the importance of data minimisation and outline IMS Health’s requirement for national, timely HES data in the following paragraphs. IMS Health requires national data to enable the end users of IMS Health’s tools to benchmark against organisations in their local area or with similar demographic characteristics. IMS Health also requires national data to inform economic analyses for inclusion in submissions to NICE, which makes decisions at a national level. HFS is intended for all chief pharmacists in NHS Trusts. The requirement for timely data is because the commissioners and providers to whom IMS Health provide IMS Health’s tools need to make decisions based on the most up-to-date information. IMS Health won an open tender to perform a medicines optimisation study for a group of cancer treatment providers. More detail on this project is given in later sections. Historic data is required to support Advanced Statistical Analysis projects, as historical data allows robust analysis of trends over time.	IMS Health will receive the data from HSCIC and will apply derivations. No linkage is carried out to other datasets. In the context of this application/agreement applying derivations does not mean linking to other patient-level information. In this application/agreement, applying derivations means that IMS Health will use non-identifiable data to derive new information. For example, length of stay is approximated using the relationship between admission and discharge dates and the cost of an admission is approximated using the NHS payment by results tariff. For data visualisation and benchmarking services, further derivations are applied to allow benchmarking and the data is presented in dashboards alongside other IMS Health and publically available data sources e.g. Quality Outcomes Framework data. All data visualisation and benchmarking tools are hosted by IMS Health. All data seen by end users is aggregated, small number are suppressed and are compliant with the HES Analysis Guide. Usage of these tools is auditable and role based access controls are applied. Customers using these tools are contractually prevented from using the data for solely commercial purposes. For advanced scientific analysis, IMS Health produce bespoke analysis for external organisations on a project by project basis. All requests for bespoke analysis are subject to review by an independent scientific advisory committee (ISEAC – details in the following paragraph) who review the proposed study design. If ISEAC approves the study, it is logged on an access control register and the IMS Health researchers are allowed to access the relevant subset of HES data. The researchers will present the results of their analysis to external organisations in the form of aggregated, small number suppressed tables compliant with the HES Analysis Guide. These outputs may also take the form of counts, proportions or formulae. Anonymised abstracts will be published on the IMS Health global bibliography 6-12 months after completion of the study. ISEAC is a group of medical and scientific advisors who are independent of IMS Health. For studies based on HES data held under this Data Sharing Agreement the role of this committee is to ensure that any study performed is complaint with this data sharing agreement and by extension the Care Act 2014. All ISEAC decisions are binding, and any studies not approved will not be performed unless revised and subsequently approved. ISEAC records of decisions can be made available to NHS Digital under the caveat that they will remain commercial in confidence.	For clarity services covered within this application only produce two types of output: • Dashboards • Aggregated tables In both cases outputs are aggregated and small numbers suppressed in line with the HES Analysis Guide. Details for each of the services are given below. Visualise Healthcare Data (VHD): VHD is an internet browser based application, an iPad application or a bespoke report. Users are given role based access to the applications. The applications allow users to produce graphical and tabular estimates of burden of disease, cost of care, common comorbidities and similar analyses. These analyses may be stratified by diagnosis, organisation and other similar parameters. Care Pathway Analyser (CPA): CPA is currently an internet browser based application and other delivery methods are in development. CPA will either be deployed directly to users or used to support consulting projects. In the former users are given role based access to the application which will allow them to analyse images of aggregated pathways. In the latter, outputs will be presentations and reports containing pathway images as well as IMS Health recommendations. Hospital Feedback Services (HFS): Version 1.0 of the HFS tool has been presented to the NHS as of December 2016 (as a browser-based application) and other delivery methods are in development. Chief pharmacists will be given role-based access to a dashboard which will show them aggregated HES data, aggregated prescribing data and performance indicators. These data will be presented in graphs and tables. It is expected that log in details for Version 1.0 will be supplied by the end of Q1 2017. Advanced Statistical Analysis The data included in advanced statistical analysis are always aggregated and small number suppressed in line with the HES Analysis Guide. These outputs are produced to meet different objectives and delivered in different ways. A health economic analysis may require analysis of the data to estimate the cost of managing a given condition then used as an input in an economic model for a NICE submission. Developing a diagnostic algorithm will result in the production of a formula which may be presented to clinicians in an Excel based calculator with an explanatory report or presentation. Many of the outputs of advanced statistical analysis are reported in journal articles or conference presentations.	IMS operates on a project by project basis. Each project using this data source must generate benefit to healthcare, for example by: • Providing detailed evidence based recommendations for how to improve care in specific organisations or therapy areas • Giving healthcare professionals (HCPs) the ability to understand their own organisation’s performance via dashboards and reports; enabling them to reduce cost whilst delivering best practice care • Providing analyses to decision making bodies such as the European Medicines Agency and the National Institute for Health and Care Excellence; in order to enable them to grant patients access to innovative medicines • Contributing to knowledge to the medical community in order to stimulate further research into improving patient care Examples of how previous projects have provided benefit to patient care are given below. Developing diagnostic pathways in Fabry Disease: IMS Health developed a diagnostic algorithm for patients with Fabry disease. In current ICD-10 coding the 4 characters code (E75.2 other sphingolipidosis) encompasses 5 different diseases: Gocher disease, Krabbe disease, Niemann-Pick disease and Metachromatic leukodystrophy. Despite the similarities in disease genesis the symptoms, treatment pathways, procedures and prognosis are different. By identifying the actual underlying disease patient would be put on the correct treatment pathway more quickly and better managed their condition. The project involved working with Lysosomal storage disorder (LSD) clinical experts to understand the different diseases, the epidemiology and the diagnosis and treatment pathway. Clinicians then worked with IMS to identify inclusion and exclusion criteria for Fabry disease based on the specialties visited by the patient, associated diagnosis codes (ICD-10 codes), procedures and treatments performed (OPCS codes), and LSD specialty centres visited (key specialist centres). The team also divided some of the variables by age of the patients to define patients for disease that typically affect certain age groups. The output was a logic-based algorithm which could be used to identify Fabry disease patients in routine clinical practice. This project was completed in March 2016, the expected benefit is an improvement in the speed and accuracy of Fabry disease diagnoses. Analysis and validation of musculoskeletal services for the NHS and Care UK: Working with Aylesbury Vale CCG, Chiltern CCG, Buckinghamshire NHS Trust & Care UK, IMS Health modelled the level of service in changing environments over the next five-year period in order to improve their long-term planning process. The analysis used HES data plus data supplied from 8 CCGs. The IMS Health team designed interventions to make the service more efficient and compiled forecasts to show the impact of these interventions on the forecast. The analysis was initially summarised in a presentation but subsequently delivered as dashboard that allowed the clients to model and understand the impact of pursuing different strategies for transformation and therefore inform decision making. For example, the model predicted that reducing the rate of inpatient spells with excess bed days had a low impact on overall MSK spend; however, reducing the rate of inpatient spells where the patient had complications or comorbidities or moving outpatient appointments to the community had a much greater potential to increase efficiency. The research proposal for this project was submitted in October 2014. The final analysis was delivered in February 2016. The expected benefit is that this tool will allow HCPs to understand how to deliver more effective cost saving programmes. Patient profiling and pathway analysis for University Hospital South Manchester: In response to a requirement from a senior clinician at the University Hospital of South Manchester (UHMS), IMS performed an exploratory analysis using VHD in pneumonia and cellulitis. In both diseases, the analysis showed that more than half of all admissions were in patients from the most deprived 20% of neighbourhoods. The analysis went on to benchmark UHSM against its peers and found it had the third highest readmissions ratio in the region. The UHSM project also included analysis of patient pathways. The analysis found that the average pneumonia pathway was 69% longer than the national average and cost the NHS 37% more. Further analysis showed 38% of pneumonia pathways in Manchester contained at least one COPD-related event; this was 10 percentage points more than the national average. On average, this group of pathways was more expensive and longer than the group without a recorded COPD event. The results of this work were presented to the Trust in February 2016. The Trust expects to reduce costs and improve patient outcomes by applying best practice from Trusts with a similar case mix. Analysis of cardiology pathways for the Heart of England NHS Foundation Trust: CPA and VHD were used to review the cardiology services in the Heart of England Foundation Trust. The analysis, combined with the Trust’s own data, aimed to improve the efficiency of care. The analysis was presented at various stages to a team from the Trust in early 2016. Following on from the analysis, IMS Health recommended providing care based on clusters of procedures as this would allow the Trust to monitor consistency more closely and improve demand forecasting. IMS Health expects that this analysis will allow the Trust to improve care by being better prepared for demand for cardiology services. Using CPA to streamline hip replacement pathways in Cambridge and Peterborough CCG: CPA in combination with HES and the CCG’s own local activity data was used to establish a gold standard pathway in Cambridgeshire and Peterborough for four providers in the region. Treatment pathway analysis and benchmarking against similar CCGs enabled them to envisage where the NHS’s Cost Improvement Programme (CIP) and the Quality, Innovation Productivity and Prevention (QIPP) programme could be delivered. In the words of the Local Chief Officer “IMS Health provided me the insight to see where CIP and QIPP could be delivered by commissioning shorter pathways in line with best practice” This work was presented in February 2016. The expected benefit is that this analysis will help HCPs deliver hip replacements safely and efficiently in line with best practice. Three further examples of projects under development and their expected benefits are: Cancer Vanguard Medicines Optimisation Project: IMS Health has won a tender with a group of NHS Trusts. The aim of the project is to optimise the use of cancer medicines and reduce the unnecessary variation in cancer care and is currently in the scoping phase. IMS Health will use Advanced Statistical Analysis and the Care Pathway Analyser tool to deliver this project; it will involve a review of medicines usage in cancer, and identification of avoidable variation. The output will be a model to reduce the cost of treating cancer to ensure clarity around best practice processes. Patient reported outcomes will also ensure that the relationship between best practice and improvement in patients’ quality of life is quantified. This analysis is being developed alongside HCPs and the expected benefit is that the results will be presented back to HCPs in a way that will allow them to improve patients’ quality of life in a cost effective manner. More information about the Cancer Vanguard can be found here - http://cancervanguard.nhs.uk/about/. Staffordshire CCGs and the Rightcare programme: IMS Health is working with the Director of Strategic Programmes for a group of CCGs including: Cannock and Chase CCG, Stafford and Surrounds CCG, South East Staffs and Seisdon Peninsula CCG. The Director would like to use CPA to support the implementation of the NHS Rightcare programme. The Director had the following to say about the initiative: “NHS Rightcare (http://www.rightcare.nhs.uk/) promotes the principle of eliminating unwarranted variation in healthcare. The IMS care pathway analysis tool allows commissioners and providers to see variation in care provided and benchmark compliance with best practice utilising national HES (Hospital Episode Statistics) data in conjunction with locally available data. It is therefore a potentially valuable tool in allowing commissioners and providers to redesign pathways to achieve high quality affordable care.” Hospital Feedback Services: IMS Health is committed to ensuring that the NHS chief pharmacists have accurate and up-to-date information in order to better manage their drug dispensing. The IMS medicines optimisation dashboard is designed to include IMS Health’s Hospital Pharmacy Audit data and National HES data. The ability to include HES data is a vital component in ensuring that the output accurately reflects the seasonal variation in hospital activity and medicines usage. For example, the antibiotic usage dashboard includes the following report: Ratio of Defined Daily Dose of all dispensed antibacterial (ATC J1) products per 1000 admissions. It is the HES data that ensures accuracy on the 1000 admissions and enables IMS Health to account for seasonal variation in the analysis. IMS Health will provide HFS to all hospital trusts. It is being designed in collaboration with the chief pharmacist community to ensure that it meets their needs. IMS Health expects HFS to benefit healthcare by allowing chief pharmacist to improve prescribing efficiency leading to a financial savings; it will also allow them to better forecast the amount of medicines required and therefore prevent waste:
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	N	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	Y	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	N	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	Y	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	N	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	Y	Background to the research: Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease. The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013). Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013) IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England. The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH). The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients. Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres. These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership. Research overview: The goal of the research is to: • Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort • Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis • Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center • Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes • Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data. Parties involved in the research: Each party in the collaboration will have a different role during the research: • STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings • IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development. • Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide. GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier. STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres. The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group. The committee will perform the following functions: 1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol 2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information) No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data. The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section. Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital. Why link data: It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty. In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions. What data is requested: The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT. In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital: 1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis) 2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH). The second cohort selection criteria are as follows: • Historical patient data for selected cohort from 2000 • No patients under the age of 18 • Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9). If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons: • Understanding impact of STHFT changes to service: Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes. Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold. • Having sufficient time to understand patient activity from onset of symptoms to diagnosis: IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm). The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis. In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons:: • Disease characteristics: Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox). • Refining the cohort based on clinical characteristics: In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible. For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts • Refining the cohort based on availability of appropriate length of historic data: The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. • Bringing the algorithm to clinical practice: If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40. Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.	To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed: 1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients 2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2. 3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT. 4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information. The applicant expects to conduct the following analysis with the data: • Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided • In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol. All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated. o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease. o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis. o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this. IMS Health Ltd will not in any circumstances attempt to re-identify the patients. All outputs will be aggregated with small number suppressed in line with the HES analysis guide. Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital	IMS Health Ltd expect to produce the following analyses: • Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided • Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided The target dissemination plan is as follows: • The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals. • The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians • Published results will be shared with the PHA UK patient advocacy group • Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available • Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful. The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals). For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings. If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions. If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.	There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research Patient pathway analysis: • The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients. • Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research • The evidence produced will help inform research direction for novel treatment in this severely under-served disease. Predictive algorithm outputs: • An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved. • By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS • However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition). • Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies. Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	IMSWorld Publications Ltd and IMS Health Ltd (referred to hereinafter as “IMS”) are specialists in the analysis of healthcare data to inform efficient allocation of medicines, understanding of safety and treatment pathways in a scientifically robust manner. IMS are part of the QuintilesIMS group. Background to The Health Improvement Network (THIN): THIN (The Health Improvement Network) is a large UK database containing primary care records that were recorded during routine clinical practice. The unique nature of the UK’s NHS allows the life-long electronic health record (EMR) of a UK patient to be held by their GP practice. THIN was set up by In Practice Systems (INPS) who provide Vision software to general practices in the UK. INPS collect pseudonymised patient data from practices that have chosen to join the THIN scheme. The data is collected from the practice's Vision clinical system on a regular basis without interruption to the running of the GP’s system and these data are collated to form the THIN database. In January 2017, the THIN database contains pseudonymised EMRs from over 16 million patients in the UK, 3.1 million of which are actively registered in a THIN contributing GP practice. The records collected for active patients are constantly updated and can be followed over time, whereas the records of patients no longer active will be included from the time they registered in the practice to the date they transferred out or when the practice stopped contributing to THIN. Patients in the database are flagged for research quality which includes having a valid start date, which is the date when the patient was registered within the GP practice (adjusted for a practice associated mortality recording dates) and an end date which is the earlier of the last practice collection or the date they transferred out of a practice. Patients that have been used in medical research have on average 9 years of follow up data recorded in THIN. Both active and inactive research quality patients are included in THIN data because, as a combined population, these patients have been shown as representative of the UK population by age, gender, medical conditions and mortality rates adjusted for demographics and social deprivation. THIN Data represents approximately 5% of the UK population with 400 active practices in the UK. For comparison, there are over 10,000 UK primary care practices. THIN data holds details of prescribed medication, symptoms, diagnoses, lab tests and additional information such as lifestyle factors, BMI and vaccinations. The THIN database remains one of the UK’s largest and most utilised longitudinal primary care databases for medical research. IMS’ strategy is to continue making improvements to THIN’s core value to research. By updating the HES data for previously linked patients, IMSWorld Publications Ltd plan to maximise the research potential of THIN through secure access to increasingly useful de-identified secondary care data. Background to THIN-HES GP practices in England who were contributing to THIN data in 2011 were invited to have their patients’ data linked with HES for the purposes of medical research. This linkage was carried out in collaboration with an Enhanced Trusted Third Party (eTTP) who had developed a secure encryption technology which enabled the THIN data linkage to take place without the need to export any patient identifiable data from the data providers (THIN practices and NHS Digital). 2.5 million patients from 158 practices in THIN were linked to HES. THIN-HES data can be used to support medical research studies investigating the relationship between primary and secondary care events. THIN-HES has details on diagnoses, emergency admissions, hospital procedures and length of stay and can be used to help validate diagnoses recorded in THIN data, confirm dates of hospital events and provide further details on hospital outcomes. Although THIN data alone may contain data with a reference to a hospitalisation episode, it is only with THIN-HES linkage that other key information (such as hospital procedures, emergency admission, length of stay) may be determined. The original THIN-HES linkage has never been refreshed or updated, and no new data linkage is being requested as part of this agreement (note IMS are requesting up to date data for those patients who are already linked). THIN data collection was approved by the NHS South East Multi-centre Research Ethics Committee (MREC) in 2003. Under the terms of this ethics approval, studies using pre-collected, pseudonymised data needed to undergo scientific review to help ensure appropriate analysis and interpretation of the data. REC has approved the THIN data collection scheme as a whole and also permitted the establishment of an Independent Scientific Review Committee to review THIN study protocols for scientific merit and feasibility. IMS have two objectives for processing the linked THIN-HES data. The two services are as follows: Service 1 Real world evidence analysis – medical research studies performed internally THIN-HES data will be used for projects or analyses performed internally approved by ISEAC and to carry out basic feasibility counts for future projects. Basic feasibility counts involve a researcher running a query against data to see if the size of the patient group present in the data is sufficient for research to be meaningful. For example they may look at the number of patients with a particular ICD10 (diagnosis) code who have taken a particular medication. The output of a feasibility count will be a number of patients e.g. 2,567. Based on this number the researcher assesses if the research is likely to be statistically valid and therefore if a research study protocol should be submitted to the approvals committee (ISEAC) for consideration and approval. The linked THIN-HES data will be used to answer focused scientific research questions with intrinsic scientific value. The governance process for approval of each individual study (see details of ISEAC below) will ensure that all studies conducted with THIN-HES data demonstrate a clear benefit to the provision of healthcare and/or the promotion of health. All research carried out under service 1 will be conducted by specialist researchers substantively employed by or on honorary contract to IMS Health Ltd/IMSWorld Publications Ltd Example of a research purpose (approved by NHS Digital) Despite a decade of continuing decline in cardiovascular (CV) disease mortality, CV deaths remain the leading cause of mortality in the UK, accounting for approximately 31% of all deaths, with ischaemic heart disease and stroke representing the vast majority (17% and 10%, respectively). Reducing low-density lipprotein cholesterol (LDL-C) with statin therapy has been shown to reduce all-cause and CV mortality, as well as CV outcomes such as non-fatal myocardial infarction (MI), coronary revascularisation procedures, and non-fatal ischaemic stroke in populations with prior atherosclerotic CV disease (ASCVD) and in certain primary-prevention populations. The high tolerability and safety of statins has also been established across these subgroups. Despite the demonstrated advantages of this treatment, appropriate statin use and atherogenic lipid level reduction remain suboptimal in clinical practice. The authors have just published a study on the retrospective examination of lipid-lowering treatment patterns in a real-world high-risk cohort in the UK in 2014: comparison with the National Institute for Health and Care Excellence (NICE) 2014 lipid modification guidelines. This study will analyse event rates of myocardial infarction, unstable angina and cardiac revascularisation in conjunction with a patients’ lipid levels and the intensity of lipid lowering (statin). The first phase of the study has utilised primary care data from The Health Improvement Network (THIN) to model subsequent CV risk in 2011 against patients’ treatment in 2010, treatment goals and unmet need (where lipid goals are not met in spite of treatment). This study will refine the cardio vascular risk model so that hospital doctors can confidently assess the likely impact of prescribing first and second line medications on a patients serum lipids (LDL cholesterol) to reduce the risk of heart attacks, strokes and mortality. In summary the model is being designed to assess the risks of high lipids v the patient benefits of reducing lipids and the cost to the NHS of further lowering lipids. The current analysis of primary care data has indicated that some “inpatient“ CV event episodes are missing from primary care data, leading to a potential underestimation of CV event rates and resultant underestimation of potential benefit to patients. For the study detailed above, IMS will utilise the linked THIN-HES data to: 1. identify CV events missing from THIN to add into the CV event model 2. to identify CV diagnosis / procedure-related admissions around the date of death (using death event date present in THIN) and other admissions (=”non CV”) around death event date to estimate the CV-related death rate in this cohort of patients to be incorporated in the mortality part of the model. Service 2 Sub-licencing the linked THIN-HES data to third parties for the performance of medical research studies. Historically IMS have provided the linked THIN-HES data to third parties via a sub-licence arrangement in the following ways: 1. Access to all THIN-HES linked data, for which data. SRC approval of a study protocol was required (contractually) before any publication or dissemination of study results were made. For the avoidance of doubt, all studies have been conducted with SRC approval and within “Permitted uses” listed previously. 2. Access to a subset of THIN-HES linked data, for the purposes of a specific study. A contract and HES DRA are also requirements in this instance. As the client requires IMS to cut and supply the data for each study, SRC approval has previously been a pre-requisite to data being shared. Detailed of the current sub-licenses issued to third parties by IMS are as follows: • 5 current sub-licencees moving to new terms • 2 sub-licencees who have not extended their agreement and who are in the process of destroying/have destroyed data. • 1 new sub-licencee All clients currently holding copies of the linked THIN-HES data have signed a new replacement sub-license agreement which has been reviewed and approved by NHS Digital. The new sub-licence includes updated terms and conditions, details of safeguards in place for the data and the organisation’s satisfactory IGToolkit score , ISO 27001 certification or approved system level security policies. The use of THIN-HES data under the new sub-licences is limited to the purposes outlined in this agreement. IMS will only carry out research on behalf of, or issue sub-licences to the following groups: Category A – Providers/commissioners of healthcare services: NHS Healthcare providers; Private secondary care providers; NHS England; Public Health England; Regulatory bodies Category B – Academics: Universities Category C – Life science industry: Pharmaceutical companies; Medical Device companies; Industry bodies Category D - Other, limited to: Patient groups; health related charities Use of the linked THIN-HES data For both Service 1 and Service 2 is limited to the following areas of medical research: epidemiology, pharmacoepidemiology, drug safety, public health research (including clinical audit), drug utilisation studies (DUS), post authorisation safety studies (PASS), outcomes research, health economics research, resource utilisation. The ISEAC committee will review all service 1 and service 2 requests. Further details on the ISEAC process is described in the processing activities section of this agreement.	THIN-HES data linkage methodology The linkage of THIN-HES data which took place in 2011 used the following methodology: (1) The patient identifier was encrypted using NHS number in the both provider data sources to create an ‘encryption key’; (2) The keys were uploaded to a secure website along with the pseudonymised THIN and HES patient IDs; (3) The matching keys were linked using only the pseudonymised THIN and HES patient IDs; (4) The pseudonymised THIN and HES ID is then used by the providers to extract the data for the linked patients. Following a thorough review of the linkage methodology by the then National Information Governance Board for Health and Social Care (NIGB), the NIGB declared that no additional ethical approval is required, since THIN will only collect and link to pseudonymised data. No new data linkage is requested as part of this agreement. IMS may wish in future to carry out the above linkage on patients within the THIN data however any such linkage would be subject to a future application to NHS digital. IMSWorld Publications Ltd and IMS Health Ltd are Joint Data Controllers for the purposes of this agreement. Both organisations are responsible for determining the purpose and manner in which THIN-HES linked data are processed. In practice, this means that employees of both organisations may work together on the data (subject to an access control process which includes training and record keeping) as well as both organisations being responsible for designing the security and access control policy. It should be noted that the separation between the organisations is purely legal as both organisations report to the same individual. No other organisation within the IMS group will have access to the record level HES data or aggregate HES data containing small numbers. Excluding the data released under sub-license, data is stored at two locations only IMS Health Ltd Pentonville Road address and also within the IMS Health 'Cage'. The Cage is hosted by Sungard Availability Services (UK) Ltd. This is the historical storage solution following on from CSDMR UK. Sungard do not process they data, they provide a 'bricks and mortar' location. Sungard can physically access the server but not the data held on it. Data can only be accessed by those on the THIN-HES access control list which is managed by IMS World Publications Ltd and audited by IMS health Ltd. Access is via the IMS infrastructure only using IMS provided equipment only. IMS will store the linked THIN-HES data for the following purposes: • For use under Service 1 and service 2 • Retaining data previously used to produce published findings, in line with recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law, to ensure reanalysis of the original dataset can feasibly be undertaken if required, subject to additional approval from NHS Digital. • The unique encrypted HESIDs need to be retained for use in future HES linkages. Without these, it would not be possible for NHS Digital to provide further linkage to the THIN data held by IMS. Data Minimisation All previously held HES data which are not linked to THIN has been securely destroyed and destruction certificates completed and provided to NHS Digital. Justification for number of HES data years held IMS holds THIN-HES data from 1997/98 to 2016/17. There are numerous scientific and medical reasons why so many years of data are required. 1) In order for real world evidence studies in patient data to be scientifically sound, all information relating to a patient’s past medical events should be considered as this will influence their doctor’s decision and affect their current care. Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis”. Previous IMS work in pulmonary arterial hypertension (PAH) suggests that patients can experience delays of on average between 1 to 4 years from onset of the first symptoms to reaching a confirmed diagnosis. Patients are often seen by multiple physicians and receive incorrect diagnoses before a confirmed diagnosis of PAH is made. In order to analyse the full patient journey IMS require historical data, dating back to when initial symptoms arose until the patient was cured or died. 2) Real world data can play a very important role in understanding disease incidence in prevalence. It is paramount to have a patient’s entire history from birth to understand incidence of disease, especially within real world data sources. If the amount of available data was reduced this would decrease the number of patients forming birth cohorts which could impact understanding of disease incidence and progression. This is particularly important in cases of rare diseases as a larger number years’ increases the likelihood of having a statistically valid number of patients within the study cohort. A rare disease is defined in the EU as affecting less than 5 in 10,000 of the general population. Fewer data years would therefore mean that patients with risk factors associated with a particular rare disease or individuals with historical diagnosis are missed. 3) IMS conducts studies looking at chronic disease progression, in long term conditions that may be present across a large portion of the patient’s life. It is therefore a requirement to utilise all historical data to fully understand the disease progression. The following are specific examples of why a greater number of years of data is essential to the quality of the research: 4) Looking at risk rates for post-surgical intervention - e.g. post-hip replacement operations (including increased length of stay, admission to intensive care, death and readmission rates) - it is important to know previous cardiovascular risk and whether it is recent or from a period much longer (e.g. ten – 15 years ago). If the number of years of data utilised was reduced patients who had an event prior to the data supplied would be assessed as having had no risk and this would invalidate all analysis of the HES data. In the example provided, one hospital might be shown to have a higher resource usage (intensive care/increased length of stay) because they are treating patients with higher risk factors and without the back data this cannot be understood or adjusted for in patient outcomes leading to a trust being incorrectly identified as having poor outcomes and performance when in fact they are dealing with more sick patients (and conversely lack of previous data will make it impossible to identify poor performing trusts). 5) For a current study “development and validation of a frailty index” (developed by Birmingham University), all available THIN/HES back-data was specifically required in order to identify all historical cardiovascular and stroke events to accurately calculate a frailty score. Even if a cardiac event occurred 10 years ago it still has a significant impact on the frailty score: for example a heart at attack at 45 is an indicator of a high risk patient even though they may not have had a subsequent event. The frailty index is used to measure the health status of older individuals - as a proxy measure of physical aging rather than chronological ageing. If it was calculated on the basis of a “year restricted” version of HES then it will underestimate “ill health” in patients and also overestimate risk in healthier patients (if all grouped together). This could result in healthier patients being offered unnecessary treatments (which is expensive to a healthcare system) or sicker patients not being offered treatments that might benefit them (increasing morbidity and death) 6) Another study - “Association between Antibiotic Prescribing in Pregnancy and Cerebral Palsy or Epilepsy in Children Born at Term” - required knowledge of all patients’s antenatal history for previous pregnancies (which can have occurred over a 20 plus year period, with anything from 1-10 other additional pregnancies). The history aided elimination of competing risk factors (present in previous events). For example women who have had several premature babies are at risk of having subsequent premature babies and this needed to be taken into account to ensure that the research outcome is correctly interpreted so that the medical professionals are able to correctly provide the mother with the correct risk assessment of taking the antibiotic. 7) For epidemiological studies longitudinal data is essential in order that a “statistical bias” is not introduced into all the analysis. For example, in a study using the CPRD database and published the BMJ (see reference below) combining primary care data with secondary care data looking benefits of cholesterol lowering with lipid lowering drugs for patients with acute myocardial events between January 2003 and March 2009. The investigation of outcomes for this study required approximately seven years of data for each individual within the cohort in order to analyse: (a) 5 year post index events heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (b) 2 year events prior to “index” of heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (c) Adjust the outcomes for risk prior to index a history of diabetes, all heart disease, stroke, peripheral arterial disease This particular study therefore required a total of 13 years of data to capture patients with an index event falling with the 6 year period (outlined above. A restriction on 5 years of HES data will reduce cardiovascular events in the outcomes, reduce the cardiovascular prior risk profile before index date, reduce the first 2 years post index event rates. This will bias this study such that those with an earlier cardiovascular risk (and therefore should be in high risk category) will be in the low risk category. This will appear to reduce the potential benefits of being on a lipid lowering drug and bias the outcomes of this analysis and produce a false analysis. If this analysis was undertaken and published indicating a reduced benefit of lipid lowering drugs then potentially many patient lives would be lost by patients not being given lipid lowering drugs who might have shown benefit if ALL the longitudinal data were present. This research carried out by the CPRD has data provided by the same GPs as THIN and links to HES in a very similar way has shown why HES data is essential for this research. Herrett E, Shah A D, Boggon R, Smeeth L, van Staa T et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013, 346:f2350 Justification for data retention Since first receiving approval in 2011, data has been supplied under sub licence as previously agreed with NHS Digital. In many of these instances, data held by sub licensees has been destroyed on the understanding that the source data would still be available should there be any need to revisit study results or questions. Sub-licensees may have received (under Service 2) either the entire linked database or a subset of this. There has been much publicity on the potential patient safety issues associated with the inability to verify study results, resulting in recommendations by regulatory bodies and also EU legislative requirements. For these reasons, data retention is the current standard in medical research and part of recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law. There are many examples where reanalysis of data has revealed patient safety issues and in some cases these drugs have been withdrawn from use. For example: adolescent use of the antidepressant drug paroxetine, the withdrawal of the anti-inflammatory drug rofecoxib due to the long term risk of heart attack, the withdrawal of the antidiabetic medication rosiglitazone due to an increased risk of heart attack and stroke. Furthermore: • NHS guidance recommends that data for study trials be retained for 10 + years http://www.noclor.nhs.uk/sites/default/files/Retention%20of%20Records%20in%20NHS%20Research.pdf • The NHS website also references requirements of EU law and UK law. COMMISSION DIRECTIVE 2003/63/EC(brought into UK law by inclusion in The Medicines for Human Use (Fees and Miscellaneous Amendments) Regulations 2003) – section 5.2(c). As a list of technical requirements, the Directive was simply added to a list of Community provisions that had to be complied with • The MRC Data toolkit recommends that data be retained for a minimum of 10 years http://www.dt-toolkit.ac.uk/researchscenarios/archiving.cfm Information governance & internal processes: Quintiles IMS group has a Global Information Assurance framework, which, in the UK, is managed by an information security management System. (ISMS). IMS Health Ltd is externally audited to ISO27001. IMS employees who access the event level THIN-HES data for Service 1 are: • Recorded on an access control register ensuring that it is possible to identify everyone with access to patient level information. • Before being given access to the THIN-HES data, employees receive information security awareness training which covers how to log incidents and how the IMS information security management system operates. • Employees also receive training on THIN; on IMS ethical and contractual obligations around the data, and on best practices for processing. The training is being updated to include THIN- HES which will need to be completed by all employees before being given access to the updated THIN-HES. • Finally a THIN-HES Confidentiality Agreement is signed by each employee which enables them to gain access to event level information. This document contains information on best practice and rules which must be abided by. • Only substantive employees of IMS Health Ltd and IMSWorld Publications Ltd who have completed the above and have been recorded on the Access Control Register will be given permission to access the THIN-HES data. • Any other researchers with a requirement to access the row level will need to sign an honorary contract (the content of which has been agreed with NHS Digital). Any employees found to be in breach of confidentiality guidelines would be managed in accordance with the main substantive terms and conditions of their employment. All employees who work for IMSWorld Publications Ltd or IMS Health Ltd are employed under the same terms and conditions of employment with the same disciplinary and confidentiality policies in place. Analytical packages such as (but not limited to) SAS are used to analyse the patient event level data. Prior to external presentation, the data are aggregated and small numbers suppressed in line with the HES Protocol Guide. Any results that are shared externally are also subject to secondary suppression which means that additional (non-small) cells in a table (or categories in a chart) may be suppressed to avoid reverse engineering of the small number. Independent Scientific Ethical Advisory Committee (ISEAC) IMSWorld Publications and IMS Health Ltd have updated the ISEAC review process for proposed THIN-HES studies and sub-licence agreements following guidance from DAAG and NHS Digital. From June 2017, all new medical research studies and new sub-license purposes will be reviewed and considered for approval by ISEAC. ISEAC terms of reference and composition have been reviewed and revised in line with NHS Digital requirements. ISEAC membership now includes patient representatives in the updated committee. All meeting minutes will be made publically available 1. Researcher access - All researchers accessing the THIN-HES data need to be a substantive employee of IMS Health Ltd or IMSWorld Publications Ltd or must have an honorary contract with either in place. All researchers accessing these data undertake training and sign additional confidentiality agreements or will have a sub-license with IMSWorld Publications Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. 2. Strong governance process - researchers only access the data for carrying research projects and for feasibility counts as described previously. Any research projects will have received approval from IMS’s Independent Scientific Ethical Advisory Committee (ISEAC) for THIN-HES. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd and IMSWorld Publications Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol. IMSWorld Publications Ltd maintains an access control register and a record of all sub-licensees where all usage of the THIN-HES data against an ISEAC or SRC approved protocol is logged and auditable . Researchers who access the event level THIN-HES data for Service 2 will be subject to the updated Sub-license T&Cs which have been agreed with NHS Digital. In addition any new sub-license applications will be required to be reviewed by ISEAC to ensure the purpose, use, safeguard and governance is in line. Transparency IMS posts summaries of THIN-HES published studies on the organisations online bibliography, which is publicly available via in the internet. For studies that aren’t published IMSWorld Publications Ltd will include the following wording in the summary section ‘This study was conducted using the THIN-HES data and is recorded on the IMS Global Bibliography for awareness’. The summaries take the form of abstracts or links to published articles, conference abstracts/posters or white papers. All summaries contain only data that is aggregated with small numbers suppressed in line with the HES Protocol Guidance. IMS World Publications Ltd and IMS Health Limited will not approve or otherwise authorise the use of the data supplied by NHS Digital for any additional purposes other than those described in this agreement.	All results of analyses performed were provided in the format of aggregated anonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Specific examples of the type of analyses that IMSWorld Publications Ltd have performed using the THIN-HES database are given in the section below. In addition, the CV risk model project will result in the results being published in a high impact peer reviewed journal e.g. Lancet, Journal of the American Medical Association (JAMA), American Heart Journal by an internationally recognised Key Opinion Leader in Cardiology. The target audience is primarily cardiologists, but will have a secondary impact with primary care doctors. Outputs from service 2 As described above, IMSWorld Publications Ltd has supplied the THIN-HES database to external researchers under Sub-license Agreements in line with the previous Data Sharing Agreement. These terms have all been updated, - as previously agreed with NHS digital. These organisations perform analyses that can only be disseminated in the form of aggregated pseudonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Published studies are added to the THIN bibliography which can be found on the IMS website which is publically available and accessed by patients. http://imsheorbibliography.com	The THIN database is extensively used by researchers to undertake population based medical research studies. There have been over 500 peer reviewed publications utilising the THIN database since its establishment in 2002, including publications in numerous peer-reviewed journals including; British Journal of General Practice, The Lancet, British Medical Journal (BMJ), Pharmacoepidemiology Drug Safety, British Journal of Dermatology, British Journal of Diabetes & Vascular Disease, Journal of Epidemiology & Community Health, The European Journal of Contraception and Reproductive Health Care, British Journal of Clinical Pharmacology and numerous conferences internationally such as; International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Society for Academic Primary Care (SAPC). Many studies using THIN data have advanced medical knowledge and understanding in both disease management and in public health, capturing the attention of prescribers, payers and key opinion leaders within the medical communities as well as helping patients better understand their medical conditions. Examples of previously published studies utilising THIN-HES include a study looking at smoking cessation in which the findings suggested that delivering smoking cessation as a routine component of hospital care, as recommended by recent National Institute for Health and Care Excellence guidance, could achieve marked reductions in the prevalence of smoking and improve the cost-effectiveness of NHS hospitals. The study was published in BMJ Thorax and has subsequently featured on the South East Coast Respiratory Programme NHS network website as well as being the subject of a NICE press release. In reference to the study, the Director of Public Health at NICE commented: “It is absurd that smoking is still being passively encouraged within hospitals. We need to end the terrible spectacle of people on drips in hospital gowns smoking outside hospital entrances… As this study highlights, there is a huge opportunity for clinicians to offer support to over 1 million smokers who present to hospital each year. By using NICE guidance, they can help make NHS secondary care an exemplar for promoting healthy behaviour.” A British Thoracic Society spokesman commented: "Smokers who are admitted to hospital include some of the poorest members of our society. This study shows that the NHS is missing regular opportunities to transform their lives through simple yet highly cost-effective measures to help them stop smoking… The health services regulators (CQC and Monitor) need to hold hospital chief executives to account and stop them ignoring the NICE recommendations to help people admitted to hospital to quit smoking." ~ Prevalence of smoking among patients treated in NHS hospitals in England in 2010/2011: a national audit. Szatkowski L1, Murray R1, Hubbard R1, Agrawal S2, Huang Y1, Britton J1. Thorax doi:10.1136/thoraxjnl-2014-206285 The above study is an example of the real world benefit to health and/or social care of using THIN-HES linked data. It reinforced the importance for Hospitals to implement the latest NICE guidance on this subject as well as raising awareness amongst clinicians, instigating important debate on the matter as well as informing patients. Another study utilised THIN-HES linked data in the development and validation of a frailty index (developed by Birmingham University) resulted in the index being recommended for use by NICE. The researchers won an industry award (EHI 2016 award for Healthcare IT Product Innovation) and the index has been recommended in the latest NICE NG56 guidelines for Multimorbidity: clinical assessment and management (https://www.nice.org.uk/guidance/ng56). An extract from that guideline reads as follows (NG56, section 1.3.2): “Consider using a validated tool such as eFI, PEONY or QAdmissions, if available in primary care electronic health records, to identify adults with multimorbidity who are at risk of adverse events such as unplanned hospital admission or admission to care homes.” ~ Development and validation of an electronic frailty index using routine primary care electronic health record data. A Clegg, C Bates, J Young, R Ryan, L Nicols, E Teale, M Mohammed, J Parry, T Marshall. http://ageing.oxfordjournals.org/content/45/3/353.full?sid=b5104b50-3c53-49c8-8cdc-f7f2e4d06653 Another study found that by utilising THIN-HES linked data, the completeness of maternity data in THIN could greatly be improved. ~ Assessing the completeness of maternity data in UK primary and secondary care: a study in The Health Improvement Network (THIN) and Hospital Episode Statistics (HES). S Man , I Petersen, I Nazareth, A Bourke, M Thompson. https://www.ucl.ac.uk/pcph/research-groups-themes/thin-pub/research_presentations/ISPOR-shukli-2012-HES_THIN In addition to the above, IMS are currently conducting a study describing pathways to complex therapy in patients with COPD. Part of this work will include examining COPD exacerbation rates. As these are usually observed in secondary care, numbers will be underestimated if data are not linked to HES data. This builds on previous work already undertaken in this area which have been published in BMJ Open. If completed, the initial publications are to be expected in 2018. As well as working closely with university academic research institutions, IMS undertakes approved Post Authorisation Safety Studies (PASS) authorised by the MHRA, European Medicines Agency and the FDA. THIN-HES linked data will support these drug safety studies which are necessary for monitoring patient safety of new medicines, and will help assess rates of serious adverse events (e.g. liver failure, stroke, myocardial infarction, neurological paralysis) that require secondary care. IMS would like to maximise the research potential of the THIN database by securely augmenting the existing primary care coverage with increasingly useful de-identified secondary care data, in order to perform studies that are beneficial to health and social care in a similar manner to the case studies described above. The additional CV risk model project will benefit patients by indicating to doctors and patients how much a further reduction in serum lipids (LDL cholesterol) could potentially reduce heart attacks, strokes and mortality. A reduction in rates of myocardial infarction and stroke will improve patient wellbeing and reduce use of in-hospital resource.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	IMSWorld Publications Ltd and IMS Health Ltd (referred to hereinafter as “IMS”) are specialists in the analysis of healthcare data to inform efficient allocation of medicines, understanding of safety and treatment pathways in a scientifically robust manner. IMS are part of the QuintilesIMS group. Background to The Health Improvement Network (THIN): THIN (The Health Improvement Network) is a large UK database containing primary care records that were recorded during routine clinical practice. The unique nature of the UK’s NHS allows the life-long electronic health record (EMR) of a UK patient to be held by their GP practice. THIN was set up by In Practice Systems (INPS) who provide Vision software to general practices in the UK. INPS collect pseudonymised patient data from practices that have chosen to join the THIN scheme. The data is collected from the practice's Vision clinical system on a regular basis without interruption to the running of the GP’s system and these data are collated to form the THIN database. In January 2017, the THIN database contains pseudonymised EMRs from over 16 million patients in the UK, 3.1 million of which are actively registered in a THIN contributing GP practice. The records collected for active patients are constantly updated and can be followed over time, whereas the records of patients no longer active will be included from the time they registered in the practice to the date they transferred out or when the practice stopped contributing to THIN. Patients in the database are flagged for research quality which includes having a valid start date, which is the date when the patient was registered within the GP practice (adjusted for a practice associated mortality recording dates) and an end date which is the earlier of the last practice collection or the date they transferred out of a practice. Patients that have been used in medical research have on average 9 years of follow up data recorded in THIN. Both active and inactive research quality patients are included in THIN data because, as a combined population, these patients have been shown as representative of the UK population by age, gender, medical conditions and mortality rates adjusted for demographics and social deprivation. THIN Data represents approximately 5% of the UK population with 400 active practices in the UK. For comparison, there are over 10,000 UK primary care practices. THIN data holds details of prescribed medication, symptoms, diagnoses, lab tests and additional information such as lifestyle factors, BMI and vaccinations. The THIN database remains one of the UK’s largest and most utilised longitudinal primary care databases for medical research. IMS’ strategy is to continue making improvements to THIN’s core value to research. By updating the HES data for previously linked patients, IMSWorld Publications Ltd plan to maximise the research potential of THIN through secure access to increasingly useful de-identified secondary care data. Background to THIN-HES GP practices in England who were contributing to THIN data in 2011 were invited to have their patients’ data linked with HES for the purposes of medical research. This linkage was carried out in collaboration with an Enhanced Trusted Third Party (eTTP) who had developed a secure encryption technology which enabled the THIN data linkage to take place without the need to export any patient identifiable data from the data providers (THIN practices and NHS Digital). 2.5 million patients from 158 practices in THIN were linked to HES. THIN-HES data can be used to support medical research studies investigating the relationship between primary and secondary care events. THIN-HES has details on diagnoses, emergency admissions, hospital procedures and length of stay and can be used to help validate diagnoses recorded in THIN data, confirm dates of hospital events and provide further details on hospital outcomes. Although THIN data alone may contain data with a reference to a hospitalisation episode, it is only with THIN-HES linkage that other key information (such as hospital procedures, emergency admission, length of stay) may be determined. The original THIN-HES linkage has never been refreshed or updated, and no new data linkage is being requested as part of this agreement (note IMS are requesting up to date data for those patients who are already linked). THIN data collection was approved by the NHS South East Multi-centre Research Ethics Committee (MREC) in 2003. Under the terms of this ethics approval, studies using pre-collected, pseudonymised data needed to undergo scientific review to help ensure appropriate analysis and interpretation of the data. REC has approved the THIN data collection scheme as a whole and also permitted the establishment of an Independent Scientific Review Committee to review THIN study protocols for scientific merit and feasibility. IMS have two objectives for processing the linked THIN-HES data. The two services are as follows: Service 1 Real world evidence analysis – medical research studies performed internally THIN-HES data will be used for projects or analyses performed internally approved by ISEAC and to carry out basic feasibility counts for future projects. Basic feasibility counts involve a researcher running a query against data to see if the size of the patient group present in the data is sufficient for research to be meaningful. For example they may look at the number of patients with a particular ICD10 (diagnosis) code who have taken a particular medication. The output of a feasibility count will be a number of patients e.g. 2,567. Based on this number the researcher assesses if the research is likely to be statistically valid and therefore if a research study protocol should be submitted to the approvals committee (ISEAC) for consideration and approval. The linked THIN-HES data will be used to answer focused scientific research questions with intrinsic scientific value. The governance process for approval of each individual study (see details of ISEAC below) will ensure that all studies conducted with THIN-HES data demonstrate a clear benefit to the provision of healthcare and/or the promotion of health. All research carried out under service 1 will be conducted by specialist researchers substantively employed by or on honorary contract to IMS Health Ltd/IMSWorld Publications Ltd Example of a research purpose (approved by NHS Digital) Despite a decade of continuing decline in cardiovascular (CV) disease mortality, CV deaths remain the leading cause of mortality in the UK, accounting for approximately 31% of all deaths, with ischaemic heart disease and stroke representing the vast majority (17% and 10%, respectively). Reducing low-density lipprotein cholesterol (LDL-C) with statin therapy has been shown to reduce all-cause and CV mortality, as well as CV outcomes such as non-fatal myocardial infarction (MI), coronary revascularisation procedures, and non-fatal ischaemic stroke in populations with prior atherosclerotic CV disease (ASCVD) and in certain primary-prevention populations. The high tolerability and safety of statins has also been established across these subgroups. Despite the demonstrated advantages of this treatment, appropriate statin use and atherogenic lipid level reduction remain suboptimal in clinical practice. The authors have just published a study on the retrospective examination of lipid-lowering treatment patterns in a real-world high-risk cohort in the UK in 2014: comparison with the National Institute for Health and Care Excellence (NICE) 2014 lipid modification guidelines. This study will analyse event rates of myocardial infarction, unstable angina and cardiac revascularisation in conjunction with a patients’ lipid levels and the intensity of lipid lowering (statin). The first phase of the study has utilised primary care data from The Health Improvement Network (THIN) to model subsequent CV risk in 2011 against patients’ treatment in 2010, treatment goals and unmet need (where lipid goals are not met in spite of treatment). This study will refine the cardio vascular risk model so that hospital doctors can confidently assess the likely impact of prescribing first and second line medications on a patients serum lipids (LDL cholesterol) to reduce the risk of heart attacks, strokes and mortality. In summary the model is being designed to assess the risks of high lipids v the patient benefits of reducing lipids and the cost to the NHS of further lowering lipids. The current analysis of primary care data has indicated that some “inpatient“ CV event episodes are missing from primary care data, leading to a potential underestimation of CV event rates and resultant underestimation of potential benefit to patients. For the study detailed above, IMS will utilise the linked THIN-HES data to: 1. identify CV events missing from THIN to add into the CV event model 2. to identify CV diagnosis / procedure-related admissions around the date of death (using death event date present in THIN) and other admissions (=”non CV”) around death event date to estimate the CV-related death rate in this cohort of patients to be incorporated in the mortality part of the model. Service 2 Sub-licencing the linked THIN-HES data to third parties for the performance of medical research studies. Historically IMS have provided the linked THIN-HES data to third parties via a sub-licence arrangement in the following ways: 1. Access to all THIN-HES linked data, for which data. SRC approval of a study protocol was required (contractually) before any publication or dissemination of study results were made. For the avoidance of doubt, all studies have been conducted with SRC approval and within “Permitted uses” listed previously. 2. Access to a subset of THIN-HES linked data, for the purposes of a specific study. A contract and HES DRA are also requirements in this instance. As the client requires IMS to cut and supply the data for each study, SRC approval has previously been a pre-requisite to data being shared. Detailed of the current sub-licenses issued to third parties by IMS are as follows: • 5 current sub-licencees moving to new terms • 2 sub-licencees who have not extended their agreement and who are in the process of destroying/have destroyed data. • 1 new sub-licencee All clients currently holding copies of the linked THIN-HES data have signed a new replacement sub-license agreement which has been reviewed and approved by NHS Digital. The new sub-licence includes updated terms and conditions, details of safeguards in place for the data and the organisation’s satisfactory IGToolkit score , ISO 27001 certification or approved system level security policies. The use of THIN-HES data under the new sub-licences is limited to the purposes outlined in this agreement. IMS will only carry out research on behalf of, or issue sub-licences to the following groups: Category A – Providers/commissioners of healthcare services: NHS Healthcare providers; Private secondary care providers; NHS England; Public Health England; Regulatory bodies Category B – Academics: Universities Category C – Life science industry: Pharmaceutical companies; Medical Device companies; Industry bodies Category D - Other, limited to: Patient groups; health related charities Use of the linked THIN-HES data For both Service 1 and Service 2 is limited to the following areas of medical research: epidemiology, pharmacoepidemiology, drug safety, public health research (including clinical audit), drug utilisation studies (DUS), post authorisation safety studies (PASS), outcomes research, health economics research, resource utilisation. The ISEAC committee will review all service 1 and service 2 requests. Further details on the ISEAC process is described in the processing activities section of this agreement.	THIN-HES data linkage methodology The linkage of THIN-HES data which took place in 2011 used the following methodology: (1) The patient identifier was encrypted using NHS number in the both provider data sources to create an ‘encryption key’; (2) The keys were uploaded to a secure website along with the pseudonymised THIN and HES patient IDs; (3) The matching keys were linked using only the pseudonymised THIN and HES patient IDs; (4) The pseudonymised THIN and HES ID is then used by the providers to extract the data for the linked patients. Following a thorough review of the linkage methodology by the then National Information Governance Board for Health and Social Care (NIGB), the NIGB declared that no additional ethical approval is required, since THIN will only collect and link to pseudonymised data. No new data linkage is requested as part of this agreement. IMS may wish in future to carry out the above linkage on patients within the THIN data however any such linkage would be subject to a future application to NHS digital. IMSWorld Publications Ltd and IMS Health Ltd are Joint Data Controllers for the purposes of this agreement. Both organisations are responsible for determining the purpose and manner in which THIN-HES linked data are processed. In practice, this means that employees of both organisations may work together on the data (subject to an access control process which includes training and record keeping) as well as both organisations being responsible for designing the security and access control policy. It should be noted that the separation between the organisations is purely legal as both organisations report to the same individual. No other organisation within the IMS group will have access to the record level HES data or aggregate HES data containing small numbers. Excluding the data released under sub-license, data is stored at two locations only IMS Health Ltd Pentonville Road address and also within the IMS Health 'Cage'. The Cage is hosted by Sungard Availability Services (UK) Ltd. This is the historical storage solution following on from CSDMR UK. Sungard do not process they data, they provide a 'bricks and mortar' location. Sungard can physically access the server but not the data held on it. Data can only be accessed by those on the THIN-HES access control list which is managed by IMS World Publications Ltd and audited by IMS health Ltd. Access is via the IMS infrastructure only using IMS provided equipment only. IMS will store the linked THIN-HES data for the following purposes: • For use under Service 1 and service 2 • Retaining data previously used to produce published findings, in line with recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law, to ensure reanalysis of the original dataset can feasibly be undertaken if required, subject to additional approval from NHS Digital. • The unique encrypted HESIDs need to be retained for use in future HES linkages. Without these, it would not be possible for NHS Digital to provide further linkage to the THIN data held by IMS. Data Minimisation All previously held HES data which are not linked to THIN has been securely destroyed and destruction certificates completed and provided to NHS Digital. Justification for number of HES data years held IMS holds THIN-HES data from 1997/98 to 2016/17. There are numerous scientific and medical reasons why so many years of data are required. 1) In order for real world evidence studies in patient data to be scientifically sound, all information relating to a patient’s past medical events should be considered as this will influence their doctor’s decision and affect their current care. Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis”. Previous IMS work in pulmonary arterial hypertension (PAH) suggests that patients can experience delays of on average between 1 to 4 years from onset of the first symptoms to reaching a confirmed diagnosis. Patients are often seen by multiple physicians and receive incorrect diagnoses before a confirmed diagnosis of PAH is made. In order to analyse the full patient journey IMS require historical data, dating back to when initial symptoms arose until the patient was cured or died. 2) Real world data can play a very important role in understanding disease incidence in prevalence. It is paramount to have a patient’s entire history from birth to understand incidence of disease, especially within real world data sources. If the amount of available data was reduced this would decrease the number of patients forming birth cohorts which could impact understanding of disease incidence and progression. This is particularly important in cases of rare diseases as a larger number years’ increases the likelihood of having a statistically valid number of patients within the study cohort. A rare disease is defined in the EU as affecting less than 5 in 10,000 of the general population. Fewer data years would therefore mean that patients with risk factors associated with a particular rare disease or individuals with historical diagnosis are missed. 3) IMS conducts studies looking at chronic disease progression, in long term conditions that may be present across a large portion of the patient’s life. It is therefore a requirement to utilise all historical data to fully understand the disease progression. The following are specific examples of why a greater number of years of data is essential to the quality of the research: 4) Looking at risk rates for post-surgical intervention - e.g. post-hip replacement operations (including increased length of stay, admission to intensive care, death and readmission rates) - it is important to know previous cardiovascular risk and whether it is recent or from a period much longer (e.g. ten – 15 years ago). If the number of years of data utilised was reduced patients who had an event prior to the data supplied would be assessed as having had no risk and this would invalidate all analysis of the HES data. In the example provided, one hospital might be shown to have a higher resource usage (intensive care/increased length of stay) because they are treating patients with higher risk factors and without the back data this cannot be understood or adjusted for in patient outcomes leading to a trust being incorrectly identified as having poor outcomes and performance when in fact they are dealing with more sick patients (and conversely lack of previous data will make it impossible to identify poor performing trusts). 5) For a current study “development and validation of a frailty index” (developed by Birmingham University), all available THIN/HES back-data was specifically required in order to identify all historical cardiovascular and stroke events to accurately calculate a frailty score. Even if a cardiac event occurred 10 years ago it still has a significant impact on the frailty score: for example a heart at attack at 45 is an indicator of a high risk patient even though they may not have had a subsequent event. The frailty index is used to measure the health status of older individuals - as a proxy measure of physical aging rather than chronological ageing. If it was calculated on the basis of a “year restricted” version of HES then it will underestimate “ill health” in patients and also overestimate risk in healthier patients (if all grouped together). This could result in healthier patients being offered unnecessary treatments (which is expensive to a healthcare system) or sicker patients not being offered treatments that might benefit them (increasing morbidity and death) 6) Another study - “Association between Antibiotic Prescribing in Pregnancy and Cerebral Palsy or Epilepsy in Children Born at Term” - required knowledge of all patients’s antenatal history for previous pregnancies (which can have occurred over a 20 plus year period, with anything from 1-10 other additional pregnancies). The history aided elimination of competing risk factors (present in previous events). For example women who have had several premature babies are at risk of having subsequent premature babies and this needed to be taken into account to ensure that the research outcome is correctly interpreted so that the medical professionals are able to correctly provide the mother with the correct risk assessment of taking the antibiotic. 7) For epidemiological studies longitudinal data is essential in order that a “statistical bias” is not introduced into all the analysis. For example, in a study using the CPRD database and published the BMJ (see reference below) combining primary care data with secondary care data looking benefits of cholesterol lowering with lipid lowering drugs for patients with acute myocardial events between January 2003 and March 2009. The investigation of outcomes for this study required approximately seven years of data for each individual within the cohort in order to analyse: (a) 5 year post index events heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (b) 2 year events prior to “index” of heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (c) Adjust the outcomes for risk prior to index a history of diabetes, all heart disease, stroke, peripheral arterial disease This particular study therefore required a total of 13 years of data to capture patients with an index event falling with the 6 year period (outlined above. A restriction on 5 years of HES data will reduce cardiovascular events in the outcomes, reduce the cardiovascular prior risk profile before index date, reduce the first 2 years post index event rates. This will bias this study such that those with an earlier cardiovascular risk (and therefore should be in high risk category) will be in the low risk category. This will appear to reduce the potential benefits of being on a lipid lowering drug and bias the outcomes of this analysis and produce a false analysis. If this analysis was undertaken and published indicating a reduced benefit of lipid lowering drugs then potentially many patient lives would be lost by patients not being given lipid lowering drugs who might have shown benefit if ALL the longitudinal data were present. This research carried out by the CPRD has data provided by the same GPs as THIN and links to HES in a very similar way has shown why HES data is essential for this research. Herrett E, Shah A D, Boggon R, Smeeth L, van Staa T et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013, 346:f2350 Justification for data retention Since first receiving approval in 2011, data has been supplied under sub licence as previously agreed with NHS Digital. In many of these instances, data held by sub licensees has been destroyed on the understanding that the source data would still be available should there be any need to revisit study results or questions. Sub-licensees may have received (under Service 2) either the entire linked database or a subset of this. There has been much publicity on the potential patient safety issues associated with the inability to verify study results, resulting in recommendations by regulatory bodies and also EU legislative requirements. For these reasons, data retention is the current standard in medical research and part of recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law. There are many examples where reanalysis of data has revealed patient safety issues and in some cases these drugs have been withdrawn from use. For example: adolescent use of the antidepressant drug paroxetine, the withdrawal of the anti-inflammatory drug rofecoxib due to the long term risk of heart attack, the withdrawal of the antidiabetic medication rosiglitazone due to an increased risk of heart attack and stroke. Furthermore: • NHS guidance recommends that data for study trials be retained for 10 + years http://www.noclor.nhs.uk/sites/default/files/Retention%20of%20Records%20in%20NHS%20Research.pdf • The NHS website also references requirements of EU law and UK law. COMMISSION DIRECTIVE 2003/63/EC(brought into UK law by inclusion in The Medicines for Human Use (Fees and Miscellaneous Amendments) Regulations 2003) – section 5.2(c). As a list of technical requirements, the Directive was simply added to a list of Community provisions that had to be complied with • The MRC Data toolkit recommends that data be retained for a minimum of 10 years http://www.dt-toolkit.ac.uk/researchscenarios/archiving.cfm Information governance & internal processes: Quintiles IMS group has a Global Information Assurance framework, which, in the UK, is managed by an information security management System. (ISMS). IMS Health Ltd is externally audited to ISO27001. IMS employees who access the event level THIN-HES data for Service 1 are: • Recorded on an access control register ensuring that it is possible to identify everyone with access to patient level information. • Before being given access to the THIN-HES data, employees receive information security awareness training which covers how to log incidents and how the IMS information security management system operates. • Employees also receive training on THIN; on IMS ethical and contractual obligations around the data, and on best practices for processing. The training is being updated to include THIN- HES which will need to be completed by all employees before being given access to the updated THIN-HES. • Finally a THIN-HES Confidentiality Agreement is signed by each employee which enables them to gain access to event level information. This document contains information on best practice and rules which must be abided by. • Only substantive employees of IMS Health Ltd and IMSWorld Publications Ltd who have completed the above and have been recorded on the Access Control Register will be given permission to access the THIN-HES data. • Any other researchers with a requirement to access the row level will need to sign an honorary contract (the content of which has been agreed with NHS Digital). Any employees found to be in breach of confidentiality guidelines would be managed in accordance with the main substantive terms and conditions of their employment. All employees who work for IMSWorld Publications Ltd or IMS Health Ltd are employed under the same terms and conditions of employment with the same disciplinary and confidentiality policies in place. Analytical packages such as (but not limited to) SAS are used to analyse the patient event level data. Prior to external presentation, the data are aggregated and small numbers suppressed in line with the HES Protocol Guide. Any results that are shared externally are also subject to secondary suppression which means that additional (non-small) cells in a table (or categories in a chart) may be suppressed to avoid reverse engineering of the small number. Independent Scientific Ethical Advisory Committee (ISEAC) IMSWorld Publications and IMS Health Ltd have updated the ISEAC review process for proposed THIN-HES studies and sub-licence agreements following guidance from DAAG and NHS Digital. From June 2017, all new medical research studies and new sub-license purposes will be reviewed and considered for approval by ISEAC. ISEAC terms of reference and composition have been reviewed and revised in line with NHS Digital requirements. ISEAC membership now includes patient representatives in the updated committee. All meeting minutes will be made publically available 1. Researcher access - All researchers accessing the THIN-HES data need to be a substantive employee of IMS Health Ltd or IMSWorld Publications Ltd or must have an honorary contract with either in place. All researchers accessing these data undertake training and sign additional confidentiality agreements or will have a sub-license with IMSWorld Publications Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. 2. Strong governance process - researchers only access the data for carrying research projects and for feasibility counts as described previously. Any research projects will have received approval from IMS’s Independent Scientific Ethical Advisory Committee (ISEAC) for THIN-HES. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd and IMSWorld Publications Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol. IMSWorld Publications Ltd maintains an access control register and a record of all sub-licensees where all usage of the THIN-HES data against an ISEAC or SRC approved protocol is logged and auditable . Researchers who access the event level THIN-HES data for Service 2 will be subject to the updated Sub-license T&Cs which have been agreed with NHS Digital. In addition any new sub-license applications will be required to be reviewed by ISEAC to ensure the purpose, use, safeguard and governance is in line. Transparency IMS posts summaries of THIN-HES published studies on the organisations online bibliography, which is publicly available via in the internet. For studies that aren’t published IMSWorld Publications Ltd will include the following wording in the summary section ‘This study was conducted using the THIN-HES data and is recorded on the IMS Global Bibliography for awareness’. The summaries take the form of abstracts or links to published articles, conference abstracts/posters or white papers. All summaries contain only data that is aggregated with small numbers suppressed in line with the HES Protocol Guidance. IMS World Publications Ltd and IMS Health Limited will not approve or otherwise authorise the use of the data supplied by NHS Digital for any additional purposes other than those described in this agreement.	All results of analyses performed were provided in the format of aggregated anonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Specific examples of the type of analyses that IMSWorld Publications Ltd have performed using the THIN-HES database are given in the section below. In addition, the CV risk model project will result in the results being published in a high impact peer reviewed journal e.g. Lancet, Journal of the American Medical Association (JAMA), American Heart Journal by an internationally recognised Key Opinion Leader in Cardiology. The target audience is primarily cardiologists, but will have a secondary impact with primary care doctors. Outputs from service 2 As described above, IMSWorld Publications Ltd has supplied the THIN-HES database to external researchers under Sub-license Agreements in line with the previous Data Sharing Agreement. These terms have all been updated, - as previously agreed with NHS digital. These organisations perform analyses that can only be disseminated in the form of aggregated pseudonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Published studies are added to the THIN bibliography which can be found on the IMS website which is publically available and accessed by patients. http://imsheorbibliography.com	The THIN database is extensively used by researchers to undertake population based medical research studies. There have been over 500 peer reviewed publications utilising the THIN database since its establishment in 2002, including publications in numerous peer-reviewed journals including; British Journal of General Practice, The Lancet, British Medical Journal (BMJ), Pharmacoepidemiology Drug Safety, British Journal of Dermatology, British Journal of Diabetes & Vascular Disease, Journal of Epidemiology & Community Health, The European Journal of Contraception and Reproductive Health Care, British Journal of Clinical Pharmacology and numerous conferences internationally such as; International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Society for Academic Primary Care (SAPC). Many studies using THIN data have advanced medical knowledge and understanding in both disease management and in public health, capturing the attention of prescribers, payers and key opinion leaders within the medical communities as well as helping patients better understand their medical conditions. Examples of previously published studies utilising THIN-HES include a study looking at smoking cessation in which the findings suggested that delivering smoking cessation as a routine component of hospital care, as recommended by recent National Institute for Health and Care Excellence guidance, could achieve marked reductions in the prevalence of smoking and improve the cost-effectiveness of NHS hospitals. The study was published in BMJ Thorax and has subsequently featured on the South East Coast Respiratory Programme NHS network website as well as being the subject of a NICE press release. In reference to the study, the Director of Public Health at NICE commented: “It is absurd that smoking is still being passively encouraged within hospitals. We need to end the terrible spectacle of people on drips in hospital gowns smoking outside hospital entrances… As this study highlights, there is a huge opportunity for clinicians to offer support to over 1 million smokers who present to hospital each year. By using NICE guidance, they can help make NHS secondary care an exemplar for promoting healthy behaviour.” A British Thoracic Society spokesman commented: "Smokers who are admitted to hospital include some of the poorest members of our society. This study shows that the NHS is missing regular opportunities to transform their lives through simple yet highly cost-effective measures to help them stop smoking… The health services regulators (CQC and Monitor) need to hold hospital chief executives to account and stop them ignoring the NICE recommendations to help people admitted to hospital to quit smoking." ~ Prevalence of smoking among patients treated in NHS hospitals in England in 2010/2011: a national audit. Szatkowski L1, Murray R1, Hubbard R1, Agrawal S2, Huang Y1, Britton J1. Thorax doi:10.1136/thoraxjnl-2014-206285 The above study is an example of the real world benefit to health and/or social care of using THIN-HES linked data. It reinforced the importance for Hospitals to implement the latest NICE guidance on this subject as well as raising awareness amongst clinicians, instigating important debate on the matter as well as informing patients. Another study utilised THIN-HES linked data in the development and validation of a frailty index (developed by Birmingham University) resulted in the index being recommended for use by NICE. The researchers won an industry award (EHI 2016 award for Healthcare IT Product Innovation) and the index has been recommended in the latest NICE NG56 guidelines for Multimorbidity: clinical assessment and management (https://www.nice.org.uk/guidance/ng56). An extract from that guideline reads as follows (NG56, section 1.3.2): “Consider using a validated tool such as eFI, PEONY or QAdmissions, if available in primary care electronic health records, to identify adults with multimorbidity who are at risk of adverse events such as unplanned hospital admission or admission to care homes.” ~ Development and validation of an electronic frailty index using routine primary care electronic health record data. A Clegg, C Bates, J Young, R Ryan, L Nicols, E Teale, M Mohammed, J Parry, T Marshall. http://ageing.oxfordjournals.org/content/45/3/353.full?sid=b5104b50-3c53-49c8-8cdc-f7f2e4d06653 Another study found that by utilising THIN-HES linked data, the completeness of maternity data in THIN could greatly be improved. ~ Assessing the completeness of maternity data in UK primary and secondary care: a study in The Health Improvement Network (THIN) and Hospital Episode Statistics (HES). S Man , I Petersen, I Nazareth, A Bourke, M Thompson. https://www.ucl.ac.uk/pcph/research-groups-themes/thin-pub/research_presentations/ISPOR-shukli-2012-HES_THIN In addition to the above, IMS are currently conducting a study describing pathways to complex therapy in patients with COPD. Part of this work will include examining COPD exacerbation rates. As these are usually observed in secondary care, numbers will be underestimated if data are not linked to HES data. This builds on previous work already undertaken in this area which have been published in BMJ Open. If completed, the initial publications are to be expected in 2018. As well as working closely with university academic research institutions, IMS undertakes approved Post Authorisation Safety Studies (PASS) authorised by the MHRA, European Medicines Agency and the FDA. THIN-HES linked data will support these drug safety studies which are necessary for monitoring patient safety of new medicines, and will help assess rates of serious adverse events (e.g. liver failure, stroke, myocardial infarction, neurological paralysis) that require secondary care. IMS would like to maximise the research potential of the THIN database by securely augmenting the existing primary care coverage with increasingly useful de-identified secondary care data, in order to perform studies that are beneficial to health and social care in a similar manner to the case studies described above. The additional CV risk model project will benefit patients by indicating to doctors and patients how much a further reduction in serum lipids (LDL cholesterol) could potentially reduce heart attacks, strokes and mortality. A reduction in rates of myocardial infarction and stroke will improve patient wellbeing and reduce use of in-hospital resource.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	IMSWorld Publications Ltd and IMS Health Ltd (referred to hereinafter as “IMS”) are specialists in the analysis of healthcare data to inform efficient allocation of medicines, understanding of safety and treatment pathways in a scientifically robust manner. IMS are part of the QuintilesIMS group. Background to The Health Improvement Network (THIN): THIN (The Health Improvement Network) is a large UK database containing primary care records that were recorded during routine clinical practice. The unique nature of the UK’s NHS allows the life-long electronic health record (EMR) of a UK patient to be held by their GP practice. THIN was set up by In Practice Systems (INPS) who provide Vision software to general practices in the UK. INPS collect pseudonymised patient data from practices that have chosen to join the THIN scheme. The data is collected from the practice's Vision clinical system on a regular basis without interruption to the running of the GP’s system and these data are collated to form the THIN database. In January 2017, the THIN database contains pseudonymised EMRs from over 16 million patients in the UK, 3.1 million of which are actively registered in a THIN contributing GP practice. The records collected for active patients are constantly updated and can be followed over time, whereas the records of patients no longer active will be included from the time they registered in the practice to the date they transferred out or when the practice stopped contributing to THIN. Patients in the database are flagged for research quality which includes having a valid start date, which is the date when the patient was registered within the GP practice (adjusted for a practice associated mortality recording dates) and an end date which is the earlier of the last practice collection or the date they transferred out of a practice. Patients that have been used in medical research have on average 9 years of follow up data recorded in THIN. Both active and inactive research quality patients are included in THIN data because, as a combined population, these patients have been shown as representative of the UK population by age, gender, medical conditions and mortality rates adjusted for demographics and social deprivation. THIN Data represents approximately 5% of the UK population with 400 active practices in the UK. For comparison, there are over 10,000 UK primary care practices. THIN data holds details of prescribed medication, symptoms, diagnoses, lab tests and additional information such as lifestyle factors, BMI and vaccinations. The THIN database remains one of the UK’s largest and most utilised longitudinal primary care databases for medical research. IMS’ strategy is to continue making improvements to THIN’s core value to research. By updating the HES data for previously linked patients, IMSWorld Publications Ltd plan to maximise the research potential of THIN through secure access to increasingly useful de-identified secondary care data. Background to THIN-HES GP practices in England who were contributing to THIN data in 2011 were invited to have their patients’ data linked with HES for the purposes of medical research. This linkage was carried out in collaboration with an Enhanced Trusted Third Party (eTTP) who had developed a secure encryption technology which enabled the THIN data linkage to take place without the need to export any patient identifiable data from the data providers (THIN practices and NHS Digital). 2.5 million patients from 158 practices in THIN were linked to HES. THIN-HES data can be used to support medical research studies investigating the relationship between primary and secondary care events. THIN-HES has details on diagnoses, emergency admissions, hospital procedures and length of stay and can be used to help validate diagnoses recorded in THIN data, confirm dates of hospital events and provide further details on hospital outcomes. Although THIN data alone may contain data with a reference to a hospitalisation episode, it is only with THIN-HES linkage that other key information (such as hospital procedures, emergency admission, length of stay) may be determined. The original THIN-HES linkage has never been refreshed or updated, and no new data linkage is being requested as part of this agreement (note IMS are requesting up to date data for those patients who are already linked). THIN data collection was approved by the NHS South East Multi-centre Research Ethics Committee (MREC) in 2003. Under the terms of this ethics approval, studies using pre-collected, pseudonymised data needed to undergo scientific review to help ensure appropriate analysis and interpretation of the data. REC has approved the THIN data collection scheme as a whole and also permitted the establishment of an Independent Scientific Review Committee to review THIN study protocols for scientific merit and feasibility. IMS have two objectives for processing the linked THIN-HES data. The two services are as follows: Service 1 Real world evidence analysis – medical research studies performed internally THIN-HES data will be used for projects or analyses performed internally approved by ISEAC and to carry out basic feasibility counts for future projects. Basic feasibility counts involve a researcher running a query against data to see if the size of the patient group present in the data is sufficient for research to be meaningful. For example they may look at the number of patients with a particular ICD10 (diagnosis) code who have taken a particular medication. The output of a feasibility count will be a number of patients e.g. 2,567. Based on this number the researcher assesses if the research is likely to be statistically valid and therefore if a research study protocol should be submitted to the approvals committee (ISEAC) for consideration and approval. The linked THIN-HES data will be used to answer focused scientific research questions with intrinsic scientific value. The governance process for approval of each individual study (see details of ISEAC below) will ensure that all studies conducted with THIN-HES data demonstrate a clear benefit to the provision of healthcare and/or the promotion of health. All research carried out under service 1 will be conducted by specialist researchers substantively employed by or on honorary contract to IMS Health Ltd/IMSWorld Publications Ltd Example of a research purpose (approved by NHS Digital) Despite a decade of continuing decline in cardiovascular (CV) disease mortality, CV deaths remain the leading cause of mortality in the UK, accounting for approximately 31% of all deaths, with ischaemic heart disease and stroke representing the vast majority (17% and 10%, respectively). Reducing low-density lipprotein cholesterol (LDL-C) with statin therapy has been shown to reduce all-cause and CV mortality, as well as CV outcomes such as non-fatal myocardial infarction (MI), coronary revascularisation procedures, and non-fatal ischaemic stroke in populations with prior atherosclerotic CV disease (ASCVD) and in certain primary-prevention populations. The high tolerability and safety of statins has also been established across these subgroups. Despite the demonstrated advantages of this treatment, appropriate statin use and atherogenic lipid level reduction remain suboptimal in clinical practice. The authors have just published a study on the retrospective examination of lipid-lowering treatment patterns in a real-world high-risk cohort in the UK in 2014: comparison with the National Institute for Health and Care Excellence (NICE) 2014 lipid modification guidelines. This study will analyse event rates of myocardial infarction, unstable angina and cardiac revascularisation in conjunction with a patients’ lipid levels and the intensity of lipid lowering (statin). The first phase of the study has utilised primary care data from The Health Improvement Network (THIN) to model subsequent CV risk in 2011 against patients’ treatment in 2010, treatment goals and unmet need (where lipid goals are not met in spite of treatment). This study will refine the cardio vascular risk model so that hospital doctors can confidently assess the likely impact of prescribing first and second line medications on a patients serum lipids (LDL cholesterol) to reduce the risk of heart attacks, strokes and mortality. In summary the model is being designed to assess the risks of high lipids v the patient benefits of reducing lipids and the cost to the NHS of further lowering lipids. The current analysis of primary care data has indicated that some “inpatient“ CV event episodes are missing from primary care data, leading to a potential underestimation of CV event rates and resultant underestimation of potential benefit to patients. For the study detailed above, IMS will utilise the linked THIN-HES data to: 1. identify CV events missing from THIN to add into the CV event model 2. to identify CV diagnosis / procedure-related admissions around the date of death (using death event date present in THIN) and other admissions (=”non CV”) around death event date to estimate the CV-related death rate in this cohort of patients to be incorporated in the mortality part of the model. Service 2 Sub-licencing the linked THIN-HES data to third parties for the performance of medical research studies. Historically IMS have provided the linked THIN-HES data to third parties via a sub-licence arrangement in the following ways: 1. Access to all THIN-HES linked data, for which data. SRC approval of a study protocol was required (contractually) before any publication or dissemination of study results were made. For the avoidance of doubt, all studies have been conducted with SRC approval and within “Permitted uses” listed previously. 2. Access to a subset of THIN-HES linked data, for the purposes of a specific study. A contract and HES DRA are also requirements in this instance. As the client requires IMS to cut and supply the data for each study, SRC approval has previously been a pre-requisite to data being shared. Detailed of the current sub-licenses issued to third parties by IMS are as follows: • 5 current sub-licencees moving to new terms • 2 sub-licencees who have not extended their agreement and who are in the process of destroying/have destroyed data. • 1 new sub-licencee All clients currently holding copies of the linked THIN-HES data have signed a new replacement sub-license agreement which has been reviewed and approved by NHS Digital. The new sub-licence includes updated terms and conditions, details of safeguards in place for the data and the organisation’s satisfactory IGToolkit score , ISO 27001 certification or approved system level security policies. The use of THIN-HES data under the new sub-licences is limited to the purposes outlined in this agreement. IMS will only carry out research on behalf of, or issue sub-licences to the following groups: Category A – Providers/commissioners of healthcare services: NHS Healthcare providers; Private secondary care providers; NHS England; Public Health England; Regulatory bodies Category B – Academics: Universities Category C – Life science industry: Pharmaceutical companies; Medical Device companies; Industry bodies Category D - Other, limited to: Patient groups; health related charities Use of the linked THIN-HES data For both Service 1 and Service 2 is limited to the following areas of medical research: epidemiology, pharmacoepidemiology, drug safety, public health research (including clinical audit), drug utilisation studies (DUS), post authorisation safety studies (PASS), outcomes research, health economics research, resource utilisation. The ISEAC committee will review all service 1 and service 2 requests. Further details on the ISEAC process is described in the processing activities section of this agreement.	THIN-HES data linkage methodology The linkage of THIN-HES data which took place in 2011 used the following methodology: (1) The patient identifier was encrypted using NHS number in the both provider data sources to create an ‘encryption key’; (2) The keys were uploaded to a secure website along with the pseudonymised THIN and HES patient IDs; (3) The matching keys were linked using only the pseudonymised THIN and HES patient IDs; (4) The pseudonymised THIN and HES ID is then used by the providers to extract the data for the linked patients. Following a thorough review of the linkage methodology by the then National Information Governance Board for Health and Social Care (NIGB), the NIGB declared that no additional ethical approval is required, since THIN will only collect and link to pseudonymised data. No new data linkage is requested as part of this agreement. IMS may wish in future to carry out the above linkage on patients within the THIN data however any such linkage would be subject to a future application to NHS digital. IMSWorld Publications Ltd and IMS Health Ltd are Joint Data Controllers for the purposes of this agreement. Both organisations are responsible for determining the purpose and manner in which THIN-HES linked data are processed. In practice, this means that employees of both organisations may work together on the data (subject to an access control process which includes training and record keeping) as well as both organisations being responsible for designing the security and access control policy. It should be noted that the separation between the organisations is purely legal as both organisations report to the same individual. No other organisation within the IMS group will have access to the record level HES data or aggregate HES data containing small numbers. Excluding the data released under sub-license, data is stored at two locations only IMS Health Ltd Pentonville Road address and also within the IMS Health 'Cage'. The Cage is hosted by Sungard Availability Services (UK) Ltd. This is the historical storage solution following on from CSDMR UK. Sungard do not process they data, they provide a 'bricks and mortar' location. Sungard can physically access the server but not the data held on it. Data can only be accessed by those on the THIN-HES access control list which is managed by IMS World Publications Ltd and audited by IMS health Ltd. Access is via the IMS infrastructure only using IMS provided equipment only. IMS will store the linked THIN-HES data for the following purposes: • For use under Service 1 and service 2 • Retaining data previously used to produce published findings, in line with recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law, to ensure reanalysis of the original dataset can feasibly be undertaken if required, subject to additional approval from NHS Digital. • The unique encrypted HESIDs need to be retained for use in future HES linkages. Without these, it would not be possible for NHS Digital to provide further linkage to the THIN data held by IMS. Data Minimisation All previously held HES data which are not linked to THIN has been securely destroyed and destruction certificates completed and provided to NHS Digital. Justification for number of HES data years held IMS holds THIN-HES data from 1997/98 to 2016/17. There are numerous scientific and medical reasons why so many years of data are required. 1) In order for real world evidence studies in patient data to be scientifically sound, all information relating to a patient’s past medical events should be considered as this will influence their doctor’s decision and affect their current care. Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis”. Previous IMS work in pulmonary arterial hypertension (PAH) suggests that patients can experience delays of on average between 1 to 4 years from onset of the first symptoms to reaching a confirmed diagnosis. Patients are often seen by multiple physicians and receive incorrect diagnoses before a confirmed diagnosis of PAH is made. In order to analyse the full patient journey IMS require historical data, dating back to when initial symptoms arose until the patient was cured or died. 2) Real world data can play a very important role in understanding disease incidence in prevalence. It is paramount to have a patient’s entire history from birth to understand incidence of disease, especially within real world data sources. If the amount of available data was reduced this would decrease the number of patients forming birth cohorts which could impact understanding of disease incidence and progression. This is particularly important in cases of rare diseases as a larger number years’ increases the likelihood of having a statistically valid number of patients within the study cohort. A rare disease is defined in the EU as affecting less than 5 in 10,000 of the general population. Fewer data years would therefore mean that patients with risk factors associated with a particular rare disease or individuals with historical diagnosis are missed. 3) IMS conducts studies looking at chronic disease progression, in long term conditions that may be present across a large portion of the patient’s life. It is therefore a requirement to utilise all historical data to fully understand the disease progression. The following are specific examples of why a greater number of years of data is essential to the quality of the research: 4) Looking at risk rates for post-surgical intervention - e.g. post-hip replacement operations (including increased length of stay, admission to intensive care, death and readmission rates) - it is important to know previous cardiovascular risk and whether it is recent or from a period much longer (e.g. ten – 15 years ago). If the number of years of data utilised was reduced patients who had an event prior to the data supplied would be assessed as having had no risk and this would invalidate all analysis of the HES data. In the example provided, one hospital might be shown to have a higher resource usage (intensive care/increased length of stay) because they are treating patients with higher risk factors and without the back data this cannot be understood or adjusted for in patient outcomes leading to a trust being incorrectly identified as having poor outcomes and performance when in fact they are dealing with more sick patients (and conversely lack of previous data will make it impossible to identify poor performing trusts). 5) For a current study “development and validation of a frailty index” (developed by Birmingham University), all available THIN/HES back-data was specifically required in order to identify all historical cardiovascular and stroke events to accurately calculate a frailty score. Even if a cardiac event occurred 10 years ago it still has a significant impact on the frailty score: for example a heart at attack at 45 is an indicator of a high risk patient even though they may not have had a subsequent event. The frailty index is used to measure the health status of older individuals - as a proxy measure of physical aging rather than chronological ageing. If it was calculated on the basis of a “year restricted” version of HES then it will underestimate “ill health” in patients and also overestimate risk in healthier patients (if all grouped together). This could result in healthier patients being offered unnecessary treatments (which is expensive to a healthcare system) or sicker patients not being offered treatments that might benefit them (increasing morbidity and death) 6) Another study - “Association between Antibiotic Prescribing in Pregnancy and Cerebral Palsy or Epilepsy in Children Born at Term” - required knowledge of all patients’s antenatal history for previous pregnancies (which can have occurred over a 20 plus year period, with anything from 1-10 other additional pregnancies). The history aided elimination of competing risk factors (present in previous events). For example women who have had several premature babies are at risk of having subsequent premature babies and this needed to be taken into account to ensure that the research outcome is correctly interpreted so that the medical professionals are able to correctly provide the mother with the correct risk assessment of taking the antibiotic. 7) For epidemiological studies longitudinal data is essential in order that a “statistical bias” is not introduced into all the analysis. For example, in a study using the CPRD database and published the BMJ (see reference below) combining primary care data with secondary care data looking benefits of cholesterol lowering with lipid lowering drugs for patients with acute myocardial events between January 2003 and March 2009. The investigation of outcomes for this study required approximately seven years of data for each individual within the cohort in order to analyse: (a) 5 year post index events heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (b) 2 year events prior to “index” of heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (c) Adjust the outcomes for risk prior to index a history of diabetes, all heart disease, stroke, peripheral arterial disease This particular study therefore required a total of 13 years of data to capture patients with an index event falling with the 6 year period (outlined above. A restriction on 5 years of HES data will reduce cardiovascular events in the outcomes, reduce the cardiovascular prior risk profile before index date, reduce the first 2 years post index event rates. This will bias this study such that those with an earlier cardiovascular risk (and therefore should be in high risk category) will be in the low risk category. This will appear to reduce the potential benefits of being on a lipid lowering drug and bias the outcomes of this analysis and produce a false analysis. If this analysis was undertaken and published indicating a reduced benefit of lipid lowering drugs then potentially many patient lives would be lost by patients not being given lipid lowering drugs who might have shown benefit if ALL the longitudinal data were present. This research carried out by the CPRD has data provided by the same GPs as THIN and links to HES in a very similar way has shown why HES data is essential for this research. Herrett E, Shah A D, Boggon R, Smeeth L, van Staa T et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013, 346:f2350 Justification for data retention Since first receiving approval in 2011, data has been supplied under sub licence as previously agreed with NHS Digital. In many of these instances, data held by sub licensees has been destroyed on the understanding that the source data would still be available should there be any need to revisit study results or questions. Sub-licensees may have received (under Service 2) either the entire linked database or a subset of this. There has been much publicity on the potential patient safety issues associated with the inability to verify study results, resulting in recommendations by regulatory bodies and also EU legislative requirements. For these reasons, data retention is the current standard in medical research and part of recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law. There are many examples where reanalysis of data has revealed patient safety issues and in some cases these drugs have been withdrawn from use. For example: adolescent use of the antidepressant drug paroxetine, the withdrawal of the anti-inflammatory drug rofecoxib due to the long term risk of heart attack, the withdrawal of the antidiabetic medication rosiglitazone due to an increased risk of heart attack and stroke. Furthermore: • NHS guidance recommends that data for study trials be retained for 10 + years http://www.noclor.nhs.uk/sites/default/files/Retention%20of%20Records%20in%20NHS%20Research.pdf • The NHS website also references requirements of EU law and UK law. COMMISSION DIRECTIVE 2003/63/EC(brought into UK law by inclusion in The Medicines for Human Use (Fees and Miscellaneous Amendments) Regulations 2003) – section 5.2(c). As a list of technical requirements, the Directive was simply added to a list of Community provisions that had to be complied with • The MRC Data toolkit recommends that data be retained for a minimum of 10 years http://www.dt-toolkit.ac.uk/researchscenarios/archiving.cfm Information governance & internal processes: Quintiles IMS group has a Global Information Assurance framework, which, in the UK, is managed by an information security management System. (ISMS). IMS Health Ltd is externally audited to ISO27001. IMS employees who access the event level THIN-HES data for Service 1 are: • Recorded on an access control register ensuring that it is possible to identify everyone with access to patient level information. • Before being given access to the THIN-HES data, employees receive information security awareness training which covers how to log incidents and how the IMS information security management system operates. • Employees also receive training on THIN; on IMS ethical and contractual obligations around the data, and on best practices for processing. The training is being updated to include THIN- HES which will need to be completed by all employees before being given access to the updated THIN-HES. • Finally a THIN-HES Confidentiality Agreement is signed by each employee which enables them to gain access to event level information. This document contains information on best practice and rules which must be abided by. • Only substantive employees of IMS Health Ltd and IMSWorld Publications Ltd who have completed the above and have been recorded on the Access Control Register will be given permission to access the THIN-HES data. • Any other researchers with a requirement to access the row level will need to sign an honorary contract (the content of which has been agreed with NHS Digital). Any employees found to be in breach of confidentiality guidelines would be managed in accordance with the main substantive terms and conditions of their employment. All employees who work for IMSWorld Publications Ltd or IMS Health Ltd are employed under the same terms and conditions of employment with the same disciplinary and confidentiality policies in place. Analytical packages such as (but not limited to) SAS are used to analyse the patient event level data. Prior to external presentation, the data are aggregated and small numbers suppressed in line with the HES Protocol Guide. Any results that are shared externally are also subject to secondary suppression which means that additional (non-small) cells in a table (or categories in a chart) may be suppressed to avoid reverse engineering of the small number. Independent Scientific Ethical Advisory Committee (ISEAC) IMSWorld Publications and IMS Health Ltd have updated the ISEAC review process for proposed THIN-HES studies and sub-licence agreements following guidance from DAAG and NHS Digital. From June 2017, all new medical research studies and new sub-license purposes will be reviewed and considered for approval by ISEAC. ISEAC terms of reference and composition have been reviewed and revised in line with NHS Digital requirements. ISEAC membership now includes patient representatives in the updated committee. All meeting minutes will be made publically available 1. Researcher access - All researchers accessing the THIN-HES data need to be a substantive employee of IMS Health Ltd or IMSWorld Publications Ltd or must have an honorary contract with either in place. All researchers accessing these data undertake training and sign additional confidentiality agreements or will have a sub-license with IMSWorld Publications Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. 2. Strong governance process - researchers only access the data for carrying research projects and for feasibility counts as described previously. Any research projects will have received approval from IMS’s Independent Scientific Ethical Advisory Committee (ISEAC) for THIN-HES. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd and IMSWorld Publications Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol. IMSWorld Publications Ltd maintains an access control register and a record of all sub-licensees where all usage of the THIN-HES data against an ISEAC or SRC approved protocol is logged and auditable . Researchers who access the event level THIN-HES data for Service 2 will be subject to the updated Sub-license T&Cs which have been agreed with NHS Digital. In addition any new sub-license applications will be required to be reviewed by ISEAC to ensure the purpose, use, safeguard and governance is in line. Transparency IMS posts summaries of THIN-HES published studies on the organisations online bibliography, which is publicly available via in the internet. For studies that aren’t published IMSWorld Publications Ltd will include the following wording in the summary section ‘This study was conducted using the THIN-HES data and is recorded on the IMS Global Bibliography for awareness’. The summaries take the form of abstracts or links to published articles, conference abstracts/posters or white papers. All summaries contain only data that is aggregated with small numbers suppressed in line with the HES Protocol Guidance. IMS World Publications Ltd and IMS Health Limited will not approve or otherwise authorise the use of the data supplied by NHS Digital for any additional purposes other than those described in this agreement.	All results of analyses performed were provided in the format of aggregated anonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Specific examples of the type of analyses that IMSWorld Publications Ltd have performed using the THIN-HES database are given in the section below. In addition, the CV risk model project will result in the results being published in a high impact peer reviewed journal e.g. Lancet, Journal of the American Medical Association (JAMA), American Heart Journal by an internationally recognised Key Opinion Leader in Cardiology. The target audience is primarily cardiologists, but will have a secondary impact with primary care doctors. Outputs from service 2 As described above, IMSWorld Publications Ltd has supplied the THIN-HES database to external researchers under Sub-license Agreements in line with the previous Data Sharing Agreement. These terms have all been updated, - as previously agreed with NHS digital. These organisations perform analyses that can only be disseminated in the form of aggregated pseudonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Published studies are added to the THIN bibliography which can be found on the IMS website which is publically available and accessed by patients. http://imsheorbibliography.com	The THIN database is extensively used by researchers to undertake population based medical research studies. There have been over 500 peer reviewed publications utilising the THIN database since its establishment in 2002, including publications in numerous peer-reviewed journals including; British Journal of General Practice, The Lancet, British Medical Journal (BMJ), Pharmacoepidemiology Drug Safety, British Journal of Dermatology, British Journal of Diabetes & Vascular Disease, Journal of Epidemiology & Community Health, The European Journal of Contraception and Reproductive Health Care, British Journal of Clinical Pharmacology and numerous conferences internationally such as; International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Society for Academic Primary Care (SAPC). Many studies using THIN data have advanced medical knowledge and understanding in both disease management and in public health, capturing the attention of prescribers, payers and key opinion leaders within the medical communities as well as helping patients better understand their medical conditions. Examples of previously published studies utilising THIN-HES include a study looking at smoking cessation in which the findings suggested that delivering smoking cessation as a routine component of hospital care, as recommended by recent National Institute for Health and Care Excellence guidance, could achieve marked reductions in the prevalence of smoking and improve the cost-effectiveness of NHS hospitals. The study was published in BMJ Thorax and has subsequently featured on the South East Coast Respiratory Programme NHS network website as well as being the subject of a NICE press release. In reference to the study, the Director of Public Health at NICE commented: “It is absurd that smoking is still being passively encouraged within hospitals. We need to end the terrible spectacle of people on drips in hospital gowns smoking outside hospital entrances… As this study highlights, there is a huge opportunity for clinicians to offer support to over 1 million smokers who present to hospital each year. By using NICE guidance, they can help make NHS secondary care an exemplar for promoting healthy behaviour.” A British Thoracic Society spokesman commented: "Smokers who are admitted to hospital include some of the poorest members of our society. This study shows that the NHS is missing regular opportunities to transform their lives through simple yet highly cost-effective measures to help them stop smoking… The health services regulators (CQC and Monitor) need to hold hospital chief executives to account and stop them ignoring the NICE recommendations to help people admitted to hospital to quit smoking." ~ Prevalence of smoking among patients treated in NHS hospitals in England in 2010/2011: a national audit. Szatkowski L1, Murray R1, Hubbard R1, Agrawal S2, Huang Y1, Britton J1. Thorax doi:10.1136/thoraxjnl-2014-206285 The above study is an example of the real world benefit to health and/or social care of using THIN-HES linked data. It reinforced the importance for Hospitals to implement the latest NICE guidance on this subject as well as raising awareness amongst clinicians, instigating important debate on the matter as well as informing patients. Another study utilised THIN-HES linked data in the development and validation of a frailty index (developed by Birmingham University) resulted in the index being recommended for use by NICE. The researchers won an industry award (EHI 2016 award for Healthcare IT Product Innovation) and the index has been recommended in the latest NICE NG56 guidelines for Multimorbidity: clinical assessment and management (https://www.nice.org.uk/guidance/ng56). An extract from that guideline reads as follows (NG56, section 1.3.2): “Consider using a validated tool such as eFI, PEONY or QAdmissions, if available in primary care electronic health records, to identify adults with multimorbidity who are at risk of adverse events such as unplanned hospital admission or admission to care homes.” ~ Development and validation of an electronic frailty index using routine primary care electronic health record data. A Clegg, C Bates, J Young, R Ryan, L Nicols, E Teale, M Mohammed, J Parry, T Marshall. http://ageing.oxfordjournals.org/content/45/3/353.full?sid=b5104b50-3c53-49c8-8cdc-f7f2e4d06653 Another study found that by utilising THIN-HES linked data, the completeness of maternity data in THIN could greatly be improved. ~ Assessing the completeness of maternity data in UK primary and secondary care: a study in The Health Improvement Network (THIN) and Hospital Episode Statistics (HES). S Man , I Petersen, I Nazareth, A Bourke, M Thompson. https://www.ucl.ac.uk/pcph/research-groups-themes/thin-pub/research_presentations/ISPOR-shukli-2012-HES_THIN In addition to the above, IMS are currently conducting a study describing pathways to complex therapy in patients with COPD. Part of this work will include examining COPD exacerbation rates. As these are usually observed in secondary care, numbers will be underestimated if data are not linked to HES data. This builds on previous work already undertaken in this area which have been published in BMJ Open. If completed, the initial publications are to be expected in 2018. As well as working closely with university academic research institutions, IMS undertakes approved Post Authorisation Safety Studies (PASS) authorised by the MHRA, European Medicines Agency and the FDA. THIN-HES linked data will support these drug safety studies which are necessary for monitoring patient safety of new medicines, and will help assess rates of serious adverse events (e.g. liver failure, stroke, myocardial infarction, neurological paralysis) that require secondary care. IMS would like to maximise the research potential of the THIN database by securely augmenting the existing primary care coverage with increasingly useful de-identified secondary care data, in order to perform studies that are beneficial to health and social care in a similar manner to the case studies described above. The additional CV risk model project will benefit patients by indicating to doctors and patients how much a further reduction in serum lipids (LDL cholesterol) could potentially reduce heart attacks, strokes and mortality. A reduction in rates of myocardial infarction and stroke will improve patient wellbeing and reduce use of in-hospital resource.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	Y	IMSWorld Publications Ltd and IMS Health Ltd (referred to hereinafter as “IMS”) are specialists in the analysis of healthcare data to inform efficient allocation of medicines, understanding of safety and treatment pathways in a scientifically robust manner. IMS are part of the QuintilesIMS group. Background to The Health Improvement Network (THIN): THIN (The Health Improvement Network) is a large UK database containing primary care records that were recorded during routine clinical practice. The unique nature of the UK’s NHS allows the life-long electronic health record (EMR) of a UK patient to be held by their GP practice. THIN was set up by In Practice Systems (INPS) who provide Vision software to general practices in the UK. INPS collect pseudonymised patient data from practices that have chosen to join the THIN scheme. The data is collected from the practice's Vision clinical system on a regular basis without interruption to the running of the GP’s system and these data are collated to form the THIN database. In January 2017, the THIN database contains pseudonymised EMRs from over 16 million patients in the UK, 3.1 million of which are actively registered in a THIN contributing GP practice. The records collected for active patients are constantly updated and can be followed over time, whereas the records of patients no longer active will be included from the time they registered in the practice to the date they transferred out or when the practice stopped contributing to THIN. Patients in the database are flagged for research quality which includes having a valid start date, which is the date when the patient was registered within the GP practice (adjusted for a practice associated mortality recording dates) and an end date which is the earlier of the last practice collection or the date they transferred out of a practice. Patients that have been used in medical research have on average 9 years of follow up data recorded in THIN. Both active and inactive research quality patients are included in THIN data because, as a combined population, these patients have been shown as representative of the UK population by age, gender, medical conditions and mortality rates adjusted for demographics and social deprivation. THIN Data represents approximately 5% of the UK population with 400 active practices in the UK. For comparison, there are over 10,000 UK primary care practices. THIN data holds details of prescribed medication, symptoms, diagnoses, lab tests and additional information such as lifestyle factors, BMI and vaccinations. The THIN database remains one of the UK’s largest and most utilised longitudinal primary care databases for medical research. IMS’ strategy is to continue making improvements to THIN’s core value to research. By updating the HES data for previously linked patients, IMSWorld Publications Ltd plan to maximise the research potential of THIN through secure access to increasingly useful de-identified secondary care data. Background to THIN-HES GP practices in England who were contributing to THIN data in 2011 were invited to have their patients’ data linked with HES for the purposes of medical research. This linkage was carried out in collaboration with an Enhanced Trusted Third Party (eTTP) who had developed a secure encryption technology which enabled the THIN data linkage to take place without the need to export any patient identifiable data from the data providers (THIN practices and NHS Digital). 2.5 million patients from 158 practices in THIN were linked to HES. THIN-HES data can be used to support medical research studies investigating the relationship between primary and secondary care events. THIN-HES has details on diagnoses, emergency admissions, hospital procedures and length of stay and can be used to help validate diagnoses recorded in THIN data, confirm dates of hospital events and provide further details on hospital outcomes. Although THIN data alone may contain data with a reference to a hospitalisation episode, it is only with THIN-HES linkage that other key information (such as hospital procedures, emergency admission, length of stay) may be determined. The original THIN-HES linkage has never been refreshed or updated, and no new data linkage is being requested as part of this agreement (note IMS are requesting up to date data for those patients who are already linked). THIN data collection was approved by the NHS South East Multi-centre Research Ethics Committee (MREC) in 2003. Under the terms of this ethics approval, studies using pre-collected, pseudonymised data needed to undergo scientific review to help ensure appropriate analysis and interpretation of the data. REC has approved the THIN data collection scheme as a whole and also permitted the establishment of an Independent Scientific Review Committee to review THIN study protocols for scientific merit and feasibility. IMS have two objectives for processing the linked THIN-HES data. The two services are as follows: Service 1 Real world evidence analysis – medical research studies performed internally THIN-HES data will be used for projects or analyses performed internally approved by ISEAC and to carry out basic feasibility counts for future projects. Basic feasibility counts involve a researcher running a query against data to see if the size of the patient group present in the data is sufficient for research to be meaningful. For example they may look at the number of patients with a particular ICD10 (diagnosis) code who have taken a particular medication. The output of a feasibility count will be a number of patients e.g. 2,567. Based on this number the researcher assesses if the research is likely to be statistically valid and therefore if a research study protocol should be submitted to the approvals committee (ISEAC) for consideration and approval. The linked THIN-HES data will be used to answer focused scientific research questions with intrinsic scientific value. The governance process for approval of each individual study (see details of ISEAC below) will ensure that all studies conducted with THIN-HES data demonstrate a clear benefit to the provision of healthcare and/or the promotion of health. All research carried out under service 1 will be conducted by specialist researchers substantively employed by or on honorary contract to IMS Health Ltd/IMSWorld Publications Ltd Example of a research purpose (approved by NHS Digital) Despite a decade of continuing decline in cardiovascular (CV) disease mortality, CV deaths remain the leading cause of mortality in the UK, accounting for approximately 31% of all deaths, with ischaemic heart disease and stroke representing the vast majority (17% and 10%, respectively). Reducing low-density lipprotein cholesterol (LDL-C) with statin therapy has been shown to reduce all-cause and CV mortality, as well as CV outcomes such as non-fatal myocardial infarction (MI), coronary revascularisation procedures, and non-fatal ischaemic stroke in populations with prior atherosclerotic CV disease (ASCVD) and in certain primary-prevention populations. The high tolerability and safety of statins has also been established across these subgroups. Despite the demonstrated advantages of this treatment, appropriate statin use and atherogenic lipid level reduction remain suboptimal in clinical practice. The authors have just published a study on the retrospective examination of lipid-lowering treatment patterns in a real-world high-risk cohort in the UK in 2014: comparison with the National Institute for Health and Care Excellence (NICE) 2014 lipid modification guidelines. This study will analyse event rates of myocardial infarction, unstable angina and cardiac revascularisation in conjunction with a patients’ lipid levels and the intensity of lipid lowering (statin). The first phase of the study has utilised primary care data from The Health Improvement Network (THIN) to model subsequent CV risk in 2011 against patients’ treatment in 2010, treatment goals and unmet need (where lipid goals are not met in spite of treatment). This study will refine the cardio vascular risk model so that hospital doctors can confidently assess the likely impact of prescribing first and second line medications on a patients serum lipids (LDL cholesterol) to reduce the risk of heart attacks, strokes and mortality. In summary the model is being designed to assess the risks of high lipids v the patient benefits of reducing lipids and the cost to the NHS of further lowering lipids. The current analysis of primary care data has indicated that some “inpatient“ CV event episodes are missing from primary care data, leading to a potential underestimation of CV event rates and resultant underestimation of potential benefit to patients. For the study detailed above, IMS will utilise the linked THIN-HES data to: 1. identify CV events missing from THIN to add into the CV event model 2. to identify CV diagnosis / procedure-related admissions around the date of death (using death event date present in THIN) and other admissions (=”non CV”) around death event date to estimate the CV-related death rate in this cohort of patients to be incorporated in the mortality part of the model. Service 2 Sub-licencing the linked THIN-HES data to third parties for the performance of medical research studies. Historically IMS have provided the linked THIN-HES data to third parties via a sub-licence arrangement in the following ways: 1. Access to all THIN-HES linked data, for which data. SRC approval of a study protocol was required (contractually) before any publication or dissemination of study results were made. For the avoidance of doubt, all studies have been conducted with SRC approval and within “Permitted uses” listed previously. 2. Access to a subset of THIN-HES linked data, for the purposes of a specific study. A contract and HES DRA are also requirements in this instance. As the client requires IMS to cut and supply the data for each study, SRC approval has previously been a pre-requisite to data being shared. Detailed of the current sub-licenses issued to third parties by IMS are as follows: • 5 current sub-licencees moving to new terms • 2 sub-licencees who have not extended their agreement and who are in the process of destroying/have destroyed data. • 1 new sub-licencee All clients currently holding copies of the linked THIN-HES data have signed a new replacement sub-license agreement which has been reviewed and approved by NHS Digital. The new sub-licence includes updated terms and conditions, details of safeguards in place for the data and the organisation’s satisfactory IGToolkit score , ISO 27001 certification or approved system level security policies. The use of THIN-HES data under the new sub-licences is limited to the purposes outlined in this agreement. IMS will only carry out research on behalf of, or issue sub-licences to the following groups: Category A – Providers/commissioners of healthcare services: NHS Healthcare providers; Private secondary care providers; NHS England; Public Health England; Regulatory bodies Category B – Academics: Universities Category C – Life science industry: Pharmaceutical companies; Medical Device companies; Industry bodies Category D - Other, limited to: Patient groups; health related charities Use of the linked THIN-HES data For both Service 1 and Service 2 is limited to the following areas of medical research: epidemiology, pharmacoepidemiology, drug safety, public health research (including clinical audit), drug utilisation studies (DUS), post authorisation safety studies (PASS), outcomes research, health economics research, resource utilisation. The ISEAC committee will review all service 1 and service 2 requests. Further details on the ISEAC process is described in the processing activities section of this agreement.	THIN-HES data linkage methodology The linkage of THIN-HES data which took place in 2011 used the following methodology: (1) The patient identifier was encrypted using NHS number in the both provider data sources to create an ‘encryption key’; (2) The keys were uploaded to a secure website along with the pseudonymised THIN and HES patient IDs; (3) The matching keys were linked using only the pseudonymised THIN and HES patient IDs; (4) The pseudonymised THIN and HES ID is then used by the providers to extract the data for the linked patients. Following a thorough review of the linkage methodology by the then National Information Governance Board for Health and Social Care (NIGB), the NIGB declared that no additional ethical approval is required, since THIN will only collect and link to pseudonymised data. No new data linkage is requested as part of this agreement. IMS may wish in future to carry out the above linkage on patients within the THIN data however any such linkage would be subject to a future application to NHS digital. IMSWorld Publications Ltd and IMS Health Ltd are Joint Data Controllers for the purposes of this agreement. Both organisations are responsible for determining the purpose and manner in which THIN-HES linked data are processed. In practice, this means that employees of both organisations may work together on the data (subject to an access control process which includes training and record keeping) as well as both organisations being responsible for designing the security and access control policy. It should be noted that the separation between the organisations is purely legal as both organisations report to the same individual. No other organisation within the IMS group will have access to the record level HES data or aggregate HES data containing small numbers. Excluding the data released under sub-license, data is stored at two locations only IMS Health Ltd Pentonville Road address and also within the IMS Health 'Cage'. The Cage is hosted by Sungard Availability Services (UK) Ltd. This is the historical storage solution following on from CSDMR UK. Sungard do not process they data, they provide a 'bricks and mortar' location. Sungard can physically access the server but not the data held on it. Data can only be accessed by those on the THIN-HES access control list which is managed by IMS World Publications Ltd and audited by IMS health Ltd. Access is via the IMS infrastructure only using IMS provided equipment only. IMS will store the linked THIN-HES data for the following purposes: • For use under Service 1 and service 2 • Retaining data previously used to produce published findings, in line with recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law, to ensure reanalysis of the original dataset can feasibly be undertaken if required, subject to additional approval from NHS Digital. • The unique encrypted HESIDs need to be retained for use in future HES linkages. Without these, it would not be possible for NHS Digital to provide further linkage to the THIN data held by IMS. Data Minimisation All previously held HES data which are not linked to THIN has been securely destroyed and destruction certificates completed and provided to NHS Digital. Justification for number of HES data years held IMS holds THIN-HES data from 1997/98 to 2016/17. There are numerous scientific and medical reasons why so many years of data are required. 1) In order for real world evidence studies in patient data to be scientifically sound, all information relating to a patient’s past medical events should be considered as this will influence their doctor’s decision and affect their current care. Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis”. Previous IMS work in pulmonary arterial hypertension (PAH) suggests that patients can experience delays of on average between 1 to 4 years from onset of the first symptoms to reaching a confirmed diagnosis. Patients are often seen by multiple physicians and receive incorrect diagnoses before a confirmed diagnosis of PAH is made. In order to analyse the full patient journey IMS require historical data, dating back to when initial symptoms arose until the patient was cured or died. 2) Real world data can play a very important role in understanding disease incidence in prevalence. It is paramount to have a patient’s entire history from birth to understand incidence of disease, especially within real world data sources. If the amount of available data was reduced this would decrease the number of patients forming birth cohorts which could impact understanding of disease incidence and progression. This is particularly important in cases of rare diseases as a larger number years’ increases the likelihood of having a statistically valid number of patients within the study cohort. A rare disease is defined in the EU as affecting less than 5 in 10,000 of the general population. Fewer data years would therefore mean that patients with risk factors associated with a particular rare disease or individuals with historical diagnosis are missed. 3) IMS conducts studies looking at chronic disease progression, in long term conditions that may be present across a large portion of the patient’s life. It is therefore a requirement to utilise all historical data to fully understand the disease progression. The following are specific examples of why a greater number of years of data is essential to the quality of the research: 4) Looking at risk rates for post-surgical intervention - e.g. post-hip replacement operations (including increased length of stay, admission to intensive care, death and readmission rates) - it is important to know previous cardiovascular risk and whether it is recent or from a period much longer (e.g. ten – 15 years ago). If the number of years of data utilised was reduced patients who had an event prior to the data supplied would be assessed as having had no risk and this would invalidate all analysis of the HES data. In the example provided, one hospital might be shown to have a higher resource usage (intensive care/increased length of stay) because they are treating patients with higher risk factors and without the back data this cannot be understood or adjusted for in patient outcomes leading to a trust being incorrectly identified as having poor outcomes and performance when in fact they are dealing with more sick patients (and conversely lack of previous data will make it impossible to identify poor performing trusts). 5) For a current study “development and validation of a frailty index” (developed by Birmingham University), all available THIN/HES back-data was specifically required in order to identify all historical cardiovascular and stroke events to accurately calculate a frailty score. Even if a cardiac event occurred 10 years ago it still has a significant impact on the frailty score: for example a heart at attack at 45 is an indicator of a high risk patient even though they may not have had a subsequent event. The frailty index is used to measure the health status of older individuals - as a proxy measure of physical aging rather than chronological ageing. If it was calculated on the basis of a “year restricted” version of HES then it will underestimate “ill health” in patients and also overestimate risk in healthier patients (if all grouped together). This could result in healthier patients being offered unnecessary treatments (which is expensive to a healthcare system) or sicker patients not being offered treatments that might benefit them (increasing morbidity and death) 6) Another study - “Association between Antibiotic Prescribing in Pregnancy and Cerebral Palsy or Epilepsy in Children Born at Term” - required knowledge of all patients’s antenatal history for previous pregnancies (which can have occurred over a 20 plus year period, with anything from 1-10 other additional pregnancies). The history aided elimination of competing risk factors (present in previous events). For example women who have had several premature babies are at risk of having subsequent premature babies and this needed to be taken into account to ensure that the research outcome is correctly interpreted so that the medical professionals are able to correctly provide the mother with the correct risk assessment of taking the antibiotic. 7) For epidemiological studies longitudinal data is essential in order that a “statistical bias” is not introduced into all the analysis. For example, in a study using the CPRD database and published the BMJ (see reference below) combining primary care data with secondary care data looking benefits of cholesterol lowering with lipid lowering drugs for patients with acute myocardial events between January 2003 and March 2009. The investigation of outcomes for this study required approximately seven years of data for each individual within the cohort in order to analyse: (a) 5 year post index events heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (b) 2 year events prior to “index” of heart attacks, unstable angina requiring hospital admissions, heart revascularisation, stroke. (c) Adjust the outcomes for risk prior to index a history of diabetes, all heart disease, stroke, peripheral arterial disease This particular study therefore required a total of 13 years of data to capture patients with an index event falling with the 6 year period (outlined above. A restriction on 5 years of HES data will reduce cardiovascular events in the outcomes, reduce the cardiovascular prior risk profile before index date, reduce the first 2 years post index event rates. This will bias this study such that those with an earlier cardiovascular risk (and therefore should be in high risk category) will be in the low risk category. This will appear to reduce the potential benefits of being on a lipid lowering drug and bias the outcomes of this analysis and produce a false analysis. If this analysis was undertaken and published indicating a reduced benefit of lipid lowering drugs then potentially many patient lives would be lost by patients not being given lipid lowering drugs who might have shown benefit if ALL the longitudinal data were present. This research carried out by the CPRD has data provided by the same GPs as THIN and links to HES in a very similar way has shown why HES data is essential for this research. Herrett E, Shah A D, Boggon R, Smeeth L, van Staa T et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013, 346:f2350 Justification for data retention Since first receiving approval in 2011, data has been supplied under sub licence as previously agreed with NHS Digital. In many of these instances, data held by sub licensees has been destroyed on the understanding that the source data would still be available should there be any need to revisit study results or questions. Sub-licensees may have received (under Service 2) either the entire linked database or a subset of this. There has been much publicity on the potential patient safety issues associated with the inability to verify study results, resulting in recommendations by regulatory bodies and also EU legislative requirements. For these reasons, data retention is the current standard in medical research and part of recommendations set out by the NHS, the Medical Research Council (MRC) and legislated by EU law. There are many examples where reanalysis of data has revealed patient safety issues and in some cases these drugs have been withdrawn from use. For example: adolescent use of the antidepressant drug paroxetine, the withdrawal of the anti-inflammatory drug rofecoxib due to the long term risk of heart attack, the withdrawal of the antidiabetic medication rosiglitazone due to an increased risk of heart attack and stroke. Furthermore: • NHS guidance recommends that data for study trials be retained for 10 + years http://www.noclor.nhs.uk/sites/default/files/Retention%20of%20Records%20in%20NHS%20Research.pdf • The NHS website also references requirements of EU law and UK law. COMMISSION DIRECTIVE 2003/63/EC(brought into UK law by inclusion in The Medicines for Human Use (Fees and Miscellaneous Amendments) Regulations 2003) – section 5.2(c). As a list of technical requirements, the Directive was simply added to a list of Community provisions that had to be complied with • The MRC Data toolkit recommends that data be retained for a minimum of 10 years http://www.dt-toolkit.ac.uk/researchscenarios/archiving.cfm Information governance & internal processes: Quintiles IMS group has a Global Information Assurance framework, which, in the UK, is managed by an information security management System. (ISMS). IMS Health Ltd is externally audited to ISO27001. IMS employees who access the event level THIN-HES data for Service 1 are: • Recorded on an access control register ensuring that it is possible to identify everyone with access to patient level information. • Before being given access to the THIN-HES data, employees receive information security awareness training which covers how to log incidents and how the IMS information security management system operates. • Employees also receive training on THIN; on IMS ethical and contractual obligations around the data, and on best practices for processing. The training is being updated to include THIN- HES which will need to be completed by all employees before being given access to the updated THIN-HES. • Finally a THIN-HES Confidentiality Agreement is signed by each employee which enables them to gain access to event level information. This document contains information on best practice and rules which must be abided by. • Only substantive employees of IMS Health Ltd and IMSWorld Publications Ltd who have completed the above and have been recorded on the Access Control Register will be given permission to access the THIN-HES data. • Any other researchers with a requirement to access the row level will need to sign an honorary contract (the content of which has been agreed with NHS Digital). Any employees found to be in breach of confidentiality guidelines would be managed in accordance with the main substantive terms and conditions of their employment. All employees who work for IMSWorld Publications Ltd or IMS Health Ltd are employed under the same terms and conditions of employment with the same disciplinary and confidentiality policies in place. Analytical packages such as (but not limited to) SAS are used to analyse the patient event level data. Prior to external presentation, the data are aggregated and small numbers suppressed in line with the HES Protocol Guide. Any results that are shared externally are also subject to secondary suppression which means that additional (non-small) cells in a table (or categories in a chart) may be suppressed to avoid reverse engineering of the small number. Independent Scientific Ethical Advisory Committee (ISEAC) IMSWorld Publications and IMS Health Ltd have updated the ISEAC review process for proposed THIN-HES studies and sub-licence agreements following guidance from DAAG and NHS Digital. From June 2017, all new medical research studies and new sub-license purposes will be reviewed and considered for approval by ISEAC. ISEAC terms of reference and composition have been reviewed and revised in line with NHS Digital requirements. ISEAC membership now includes patient representatives in the updated committee. All meeting minutes will be made publically available 1. Researcher access - All researchers accessing the THIN-HES data need to be a substantive employee of IMS Health Ltd or IMSWorld Publications Ltd or must have an honorary contract with either in place. All researchers accessing these data undertake training and sign additional confidentiality agreements or will have a sub-license with IMSWorld Publications Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. 2. Strong governance process - researchers only access the data for carrying research projects and for feasibility counts as described previously. Any research projects will have received approval from IMS’s Independent Scientific Ethical Advisory Committee (ISEAC) for THIN-HES. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd and IMSWorld Publications Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol. IMSWorld Publications Ltd maintains an access control register and a record of all sub-licensees where all usage of the THIN-HES data against an ISEAC or SRC approved protocol is logged and auditable . Researchers who access the event level THIN-HES data for Service 2 will be subject to the updated Sub-license T&Cs which have been agreed with NHS Digital. In addition any new sub-license applications will be required to be reviewed by ISEAC to ensure the purpose, use, safeguard and governance is in line. Transparency IMS posts summaries of THIN-HES published studies on the organisations online bibliography, which is publicly available via in the internet. For studies that aren’t published IMSWorld Publications Ltd will include the following wording in the summary section ‘This study was conducted using the THIN-HES data and is recorded on the IMS Global Bibliography for awareness’. The summaries take the form of abstracts or links to published articles, conference abstracts/posters or white papers. All summaries contain only data that is aggregated with small numbers suppressed in line with the HES Protocol Guidance. IMS World Publications Ltd and IMS Health Limited will not approve or otherwise authorise the use of the data supplied by NHS Digital for any additional purposes other than those described in this agreement.	All results of analyses performed were provided in the format of aggregated anonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Specific examples of the type of analyses that IMSWorld Publications Ltd have performed using the THIN-HES database are given in the section below. In addition, the CV risk model project will result in the results being published in a high impact peer reviewed journal e.g. Lancet, Journal of the American Medical Association (JAMA), American Heart Journal by an internationally recognised Key Opinion Leader in Cardiology. The target audience is primarily cardiologists, but will have a secondary impact with primary care doctors. Outputs from service 2 As described above, IMSWorld Publications Ltd has supplied the THIN-HES database to external researchers under Sub-license Agreements in line with the previous Data Sharing Agreement. These terms have all been updated, - as previously agreed with NHS digital. These organisations perform analyses that can only be disseminated in the form of aggregated pseudonymised outputs e.g. presentations, spreadsheets, word documents and other formal documentation. As a derivative, they are also used to create conference posters, white papers and scientific peer reviewed publications. Published studies are added to the THIN bibliography which can be found on the IMS website which is publically available and accessed by patients. http://imsheorbibliography.com	The THIN database is extensively used by researchers to undertake population based medical research studies. There have been over 500 peer reviewed publications utilising the THIN database since its establishment in 2002, including publications in numerous peer-reviewed journals including; British Journal of General Practice, The Lancet, British Medical Journal (BMJ), Pharmacoepidemiology Drug Safety, British Journal of Dermatology, British Journal of Diabetes & Vascular Disease, Journal of Epidemiology & Community Health, The European Journal of Contraception and Reproductive Health Care, British Journal of Clinical Pharmacology and numerous conferences internationally such as; International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE), International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Society for Academic Primary Care (SAPC). Many studies using THIN data have advanced medical knowledge and understanding in both disease management and in public health, capturing the attention of prescribers, payers and key opinion leaders within the medical communities as well as helping patients better understand their medical conditions. Examples of previously published studies utilising THIN-HES include a study looking at smoking cessation in which the findings suggested that delivering smoking cessation as a routine component of hospital care, as recommended by recent National Institute for Health and Care Excellence guidance, could achieve marked reductions in the prevalence of smoking and improve the cost-effectiveness of NHS hospitals. The study was published in BMJ Thorax and has subsequently featured on the South East Coast Respiratory Programme NHS network website as well as being the subject of a NICE press release. In reference to the study, the Director of Public Health at NICE commented: “It is absurd that smoking is still being passively encouraged within hospitals. We need to end the terrible spectacle of people on drips in hospital gowns smoking outside hospital entrances… As this study highlights, there is a huge opportunity for clinicians to offer support to over 1 million smokers who present to hospital each year. By using NICE guidance, they can help make NHS secondary care an exemplar for promoting healthy behaviour.” A British Thoracic Society spokesman commented: "Smokers who are admitted to hospital include some of the poorest members of our society. This study shows that the NHS is missing regular opportunities to transform their lives through simple yet highly cost-effective measures to help them stop smoking… The health services regulators (CQC and Monitor) need to hold hospital chief executives to account and stop them ignoring the NICE recommendations to help people admitted to hospital to quit smoking." ~ Prevalence of smoking among patients treated in NHS hospitals in England in 2010/2011: a national audit. Szatkowski L1, Murray R1, Hubbard R1, Agrawal S2, Huang Y1, Britton J1. Thorax doi:10.1136/thoraxjnl-2014-206285 The above study is an example of the real world benefit to health and/or social care of using THIN-HES linked data. It reinforced the importance for Hospitals to implement the latest NICE guidance on this subject as well as raising awareness amongst clinicians, instigating important debate on the matter as well as informing patients. Another study utilised THIN-HES linked data in the development and validation of a frailty index (developed by Birmingham University) resulted in the index being recommended for use by NICE. The researchers won an industry award (EHI 2016 award for Healthcare IT Product Innovation) and the index has been recommended in the latest NICE NG56 guidelines for Multimorbidity: clinical assessment and management (https://www.nice.org.uk/guidance/ng56). An extract from that guideline reads as follows (NG56, section 1.3.2): “Consider using a validated tool such as eFI, PEONY or QAdmissions, if available in primary care electronic health records, to identify adults with multimorbidity who are at risk of adverse events such as unplanned hospital admission or admission to care homes.” ~ Development and validation of an electronic frailty index using routine primary care electronic health record data. A Clegg, C Bates, J Young, R Ryan, L Nicols, E Teale, M Mohammed, J Parry, T Marshall. http://ageing.oxfordjournals.org/content/45/3/353.full?sid=b5104b50-3c53-49c8-8cdc-f7f2e4d06653 Another study found that by utilising THIN-HES linked data, the completeness of maternity data in THIN could greatly be improved. ~ Assessing the completeness of maternity data in UK primary and secondary care: a study in The Health Improvement Network (THIN) and Hospital Episode Statistics (HES). S Man , I Petersen, I Nazareth, A Bourke, M Thompson. https://www.ucl.ac.uk/pcph/research-groups-themes/thin-pub/research_presentations/ISPOR-shukli-2012-HES_THIN In addition to the above, IMS are currently conducting a study describing pathways to complex therapy in patients with COPD. Part of this work will include examining COPD exacerbation rates. As these are usually observed in secondary care, numbers will be underestimated if data are not linked to HES data. This builds on previous work already undertaken in this area which have been published in BMJ Open. If completed, the initial publications are to be expected in 2018. As well as working closely with university academic research institutions, IMS undertakes approved Post Authorisation Safety Studies (PASS) authorised by the MHRA, European Medicines Agency and the FDA. THIN-HES linked data will support these drug safety studies which are necessary for monitoring patient safety of new medicines, and will help assess rates of serious adverse events (e.g. liver failure, stroke, myocardial infarction, neurological paralysis) that require secondary care. IMS would like to maximise the research potential of the THIN database by securely augmenting the existing primary care coverage with increasingly useful de-identified secondary care data, in order to perform studies that are beneficial to health and social care in a similar manner to the case studies described above. The additional CV risk model project will benefit patients by indicating to doctors and patients how much a further reduction in serum lipids (LDL cholesterol) could potentially reduce heart attacks, strokes and mortality. A reduction in rates of myocardial infarction and stroke will improve patient wellbeing and reduce use of in-hospital resource.
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	IMS Health Ltd is an information and technology company serving the health care industry. IMS Health Ltd produces a longitudinal research database, Hospital Treatment Insights (HTI) and in collaboration with the CPRD produces HTI-CPRD-GOLD. This database contains unique information on diagnosis, treatment and drug usage across primary and secondary care in England and HTI is currently the only routinely collected population based database available for monitoring the safety of medicines used in the secondary care setting. NHS Digital acts as a Trusted Third Party and provides linkage services for both HTI and HTI-CPRD-GOLD datasets. The HTI data links record level pseudonymised, non-sensitive HES data to IMS Health Ltd’s unique database of hospital prescribing data. The HTI-CPRD-GOLD data is a subset of the HTI data and links record level HTI data to CPRD-GOLD data. Currently there are 6.4 million patients in HTI data and 650,000 patients in HTI-CPRD-GOLD. The HTI and HTI-CPRD-GOLD data are treated as separate databases for study purposes so a researcher must specify which data they are requesting access to. Researchers accessing these datasets will be substantive employees of IMS Health Ltd or external researchers who have signed an NHS Digital approved honorary contract with IMS Health Ltd. 1. Data will be used to undertake a programme of research studies, in two main areas, aimed at promoting public health which will include: Advanced statistical analysis • Epidemiology • natural history of disease • health economics and outcomes research • drug exposures Drug safety monitoring • monitoring of adverse events for newly licenced drugs prescribed in secondary care • pharmacovigilance In all cases, purposes are restricted to those for the provision of health care or adult social care, or the promotion of health. Access to the data will be as follows: 1. Regulatory Authorities. Regulators such as the Medicines and Healthcare Products Regulatory Agency (MHRA) and the European Medicines Agency (EMA) will investigate potential adverse drug reactions (ADR) in marketed products. When new ADRs are discovered regulators can recommend actions to limit harm to patients exposed to the products. Responsiveness is key as products are already marketed meaning that further events could occur immediately. Therefore it is crucial to have data, like HTI and HTI-CPRD-GOLD, readily available that will allow direct investigation of ADR or evaluation of the likely public health impact should the potential ADR be established as a real effect. Examples of current studies: IMS Health Ltd is in discussions with researchers who have a special interest in drug safety with a view to conducting a proof of concept drug safety study in HTI. Regulators, such as the MHRA and EMA, have also expressed an interest in using HTI data in their drug safety activities. They currently have access to data sources that collect information on drug exposure and clinical events in primary care. However, these regulators are also responsible for pharmacovigilance of products that are only used in a hospital setting. Currently they have no directly accessible data on the use of these drugs and clinical events in patients exposed to the products except those supplied via spontaneous reporting systems. The latter are frequently the source of signals regarding potential ADRs and hence have limited use in further investigation of the signal. For this reason regulators are in need of information on drug use in a hospital setting. Thus access to HTI data has an important role in improving the use of medicines used in hospitals and could have a significant impact on public health. 2. Arm’s Length Bodies (ALBs) involved in Health and Social Care. ALBs, such as NICE, can use HTI to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. HTI will allow the study of key performance measures used by the NHS, in association with pharmaceutical treatments in the following areas: • Uptake and utilisation of new and existing therapies • Medical vs surgical treatment rates associated with specific pharmaceutical treatments • Readmission rates associated with specific pharmaceutical treatments • Rates of elective vs non elective admissions in patients following different treatment regimens IMS Health Ltd is currently working with an academic researcher who sits on NICE’s appraisal committee to investigate equity of access to high cost drugs. This work would help to identify trends and variation in secondary care prescribing of high cost drugs across the country and by socioeconomic status and could be used to support national policy. 3. Medical researchers from academia and other types of organisation such as patient groups, charitable trusts and pharmaceutical companies. HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. The topic of sustainability will be a focus area for researchers, particularly as 94% of new molecular entities will be for specialist care delivered within the hospital setting. Current academic relationships: London School of Hygiene and Tropical Medicine (Faculty of Epidemiology and Population Health) ʹworking with the epidemiology group to study the cardiovascular outcomes of varying cancer treatments in survivors of breast cancer. University College London (Dept. of Public Health) ʹa validation study to determine the strengths and limitations of the data for antibiotic research also a second piece of work on babies diagnosed with Respiratory syncytial virus treated with Pavalizumab. Clinical Practice Research Datalink – A validation study looking at whether patients discharged from hospital continue their medication in primary care. A feasibility study looking at NOAC prescribing on discharge has already been conducted and a full study will be undertaken using the updated data. . There are controls in place to ensure secure access to the data: 1. Researcher access - the HTI and HTI-CPRD-GOLD databases contain pseudonymised data. All researchers accessing the data need to be substantive employees of IMS Health Ltd or must have an honorary contract with IMS Health Ltd in place. All researchers accessing these data undertake training and sign additional confidentiality agreements with IMS Health Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. IMS Health Ltd does not permit any access to pseudonymised patient record-level data from outside of the UK. 2. Strong internal governance process - researchers only access the HTI and HTI-CPRD-GOLD data for single-study research projects that have received approval from IMS’ Independent Scientific Ethical Advisory Committee (ISEAC) for HTI studies and the CPRD͛s Independent Scientific Advisory Committee (ISAC) for HTI-CPRD-GOLD studies. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol and statistical analysis plan (SAP). IMS Health Ltd maintains an access control register and all usage of the database against an ISEAC or ISAC approved protocol is logged and auditable. Where appropriate, researchers accessing HTI or HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included on the publically available IMS global bibliography. IMS Health Ltd will share these findings with participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd will not make the outputs of safety studies publically available to avoid generating undue public concern before guidance is issued by regulatory agencies. Data minimisation: IMS Health Ltd understand the importance of data minimisation and have taken steps to reduce the number of HES records requested. IMS is requesting access to the following data: • HES records, either in an HTI trust or a non-HTI Trust, that can be linked to a pharmacy record • All other HES records from HTI Trusts that can't be linked to a pharmacy record. These records will only be used for data validation purposes to compare the % of linkage across different trusts and therapy areas. These records will only be accessible to researchers for data validation purposes and will be used to indicate whether the data is high quality enough for medical research studies. Justification for historical data - IMS Health Ltd uses historical HES data (from 2005 where available) in HTI studies in order to identify diagnosis prior to patients receiving a drug, determine any co-morbid conditions and identify the date when a patient first had a secondary care visit for a particular diagnosis (index date). HTI has been used to conduct studies on drug treatment for chronic conditions including psoriasis, rheumatoid arthritis, multiple sclerosis and ulcerative colitis. Patients with chronic diseases have these conditions for life so it is important to have the maximum number of years of back data in order to conduct studies rigorously. When researchers conduct studies using HTI they need to establish the index date of a patient at the beginning of treatment or diagnosis in order to determine progress and treatment efficacy over a follow up period. They also need to understand if patients have a history of serious comorbid conditions e.g. if a patient was hospitalised 10 years ago for a stroke then this needs to be taken into account. By answering these questions researchers are able to build cohorts for studies with the right type of characteristics. If historical HES data was not provided then researchers could miss important events which would then not be adjusted for in study results. In addition the historical data will be used to detect rare, delayed adverse events. Researchers from regulatory agencies need access to historical HES data in the HTI database in order to monitor drug safety, particularly of rare and delayed adverse events which may take many years to develop. Further scientific need for the historical data – • Historic data is required to support Advanced Statistical Analysis projects and safety studies, as historical data allows robust analysis of trends over time. • Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” • Historical data is needed for patients with chronic conditions to understand disease progression and can be used to investigate how the usage of different treatments impacts the typical time of disease progression • Historical data is needed to understand previous and co-morbid conditions in order to adjust for these in the research study outcome. For example a trust could be incorrectly identified as having poor outcomes or performance when they are in fact treating sicker or higher risk patients e.g. patients with previous cardiovascular and stroke events. This information in also needed to ensure the research outcome is correctly interpreted so that the medical professionals are able to provide the most appropriate care for patients e.g. a medication may be considered to have higher risk profile in a certain patient population • Historical data provides extended longitudinal coverage to allow researchers to look at delayed adverse events or outcomes which have a long latency period from the time of exposure to manifestation. Further justification for the historical data requirement - 1. HTI is unique in that it enables the study of hospital only medicines in a real world setting. This is very important because the patients receiving these medicines don’t always reflect those that the medicines are tested in. Many HTI studies are observational studies of the way in which drugs are used i.e. characterising the patients that receive them and then following those patients to see whether or not they benefit from the drug(s) in question. These studies are conducted over a specified study period i.e. a time frame over which the observation takes place. Setting the time period requires a starting point or ‘index’ date. This is usually the date at which a diagnosis has been made for which the drug being studied is a treatment. For chronic conditions diagnoses are made early in the patients’ history and the historic HES data enables an accurate index date to be set. Without the historic data, important information around the diagnosis of the disease would be missed. 2. HTI has been used to study the use of biologics in the treatment of autoimmune diseases such as ulcerative colitis and rheumatoid arthritis. These are expensive hospital only drugs and are not the first line of therapy for these diseases. The studies we have conducted have looked at healthcare resource use prior to the administration of these drugs i.e. the treatment pathway leading up to the administration of these drugs. Without the historic HES data, these studies would not be possible. 3. One very important use of HTI is in drug safety studies. These studies can sometimes involve identifying potential ‘signals’ or side effects of a drug. For these studies it is vital to access as much of a patient’s past medical history as possible. For example, some new drugs may have the potential to cause cardiovascular side effects, in order to evaluate these it would be important to know if a patients has suffered from cardiovascular problems in the past and to include as much detail as possible around those events into the analysis. 4. Some studies look at the difference in outcomes associated with different treatment options for a particular disease i.e. what are the characteristics of patients that respond better to one treatment over another. These studies are important because they can help doctors decide which treatment option would be more suitable for a patient. In order to conduct these studies with rigor and as much accuracy as possible, it is important to take into account every aspect the patient’s health state from their history because the past medical history affects the decisions that doctors take for current and future drug therapy. 5. Large databases like HTI are often used to conduct observational epidemiological studies. The starting point of these studies requires building a cohort of study patients based on strict inclusion and exclusion criteria. Without access to the past medical history, it is difficult to ensure that the patients included in the study are the ‘right’ patients and the study results could be biased. 6. For the study of chronic and rare diseases and delayed side effects of medicines, a long past medical history is important because the lead up to diagnosis of such conditions can be complex and lengthy. 7. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” 8. Past medical history often includes risk factors, co-morbidities and confounders that are important to adjust for when analysing data in order to ensure that conclusions drawn from the research are generalizable and robust.	Processing Activities Data flow Each month, participating trusts provide three files to NHS Digital. The 'TRUSTED' file contains patient identifiable hospital pharmacy issues data, which is used for the subsequent linkage to HES. The 'ISSUES' file is a non-identifiable version of the TRUSTED file which NHS Digital provides onward to IMS Health Ltd, and also checks against the TRUSTED file to ensure the payload data in the two files are consistent. A third data definition file ͚’DEFS͛’ is also provided to NHS Digital which is forwarded to IMS Health Ltd. The definitions file contains details of the drug such as name and the ward that issued it, does not contain identifiable data. The ISSUES and DEFS files (both of which contain no identifiable data) are received by IMS Health Ltd for their hospital pharmacy audit work, which is outside the scope of this agreement. Hospital prescribing and HES data are linked by NHS Digital and data are pseudonymised before being passed on to IMS Health Ltd on a quarterly basis. Once received, these data are downloaded via SFTP to a secure server within an ISO27001 accredited environment. Security measures include: • Access authentication • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions Remote Access to IMS ISO27001 compliant environment: Researchers access the IS027001 environment remotely via a secure portal. Researchers are able to query the data within this environment and create patient cohorts for further study. Data cannot be copied from the secure environment and usage of the secure environment is auditable. Researchers can export aggregated, small number suppressed data from the secure environment and a record of all exports is kept for monitoring and audit purposes. External Researcher access: Access to the data is only permitted for substantive IMS Health Ltd employees and researchers working under honorary contract to IMS Health Ltd. The honorary contract in use has previously been approved by DAAG and obligates the researcher to adhere to the terms and conditions of IMS Health Ltd.’s DSA. Honorary contractors are subject to the same access controls as substantive IMS Health Ltd employees. They are provided with a username and password and access the data through the secure portal. If an honorary contractor is accessing data from any location apart from the IMS Health Ltd office they will be required to provide details of the processing location in their honorary contract. IMS Health Ltd validates that the data processing location listed has appropriate security measures in place such as ISO27001 or IG Toolkit before access to the data is granted. Access to the data is not permitted from outside the UK. IMS Health Ltd reserves the right to undertake an audit of the honorary contractor at any time to ensure that appropriate security measures are in place and that all terms of the agreement are being abided by (such as agreed processing location). IMS Health Limited are not permitted to approve or otherwise authorise the use of the data supplied by NHS Digital (or predecessor organisations) for any additional purposes other than those described in the Purpose/Methods/Outputs section. No new projects or purposes are permitted under this agreement. Data cannot be used for solely commercial purposes as per the requirements of the Care Act 2014 Clause for preventing commercial use IMS Health Ltd will to include the wording below in contracts with external organisations. “NHS Digital provides IMS Health Ltd with Hospital Episode Statistics in accordance with the amended section 261 of the Health and Social Care Act 2012 i.e. “if it considers that disseminating the information would be for the purposes of— a) the provision of health care or adult social care, or b) the promotion of health.” Therefore, the CLIENT is not permitted to use the HES analytical services provided by IMS Health Ltd to do the following: • analysis for insurance companies • analysis to identify or communicate with directly with patients (or groups thereof) for purposes other than their direct care • analysis to identify or communicate with clinicians for solely sales or marketing • analysis to quantify the impact of marketing campaigns • analysis to inform salesforce structure or to understand salesforce performance” Data sharing with third party organisations under sub-licence is not permitted under this agreement. Record level data or aggregate data containing small numbers will not be shared within the IMS group of organisations.	5c. Specific Outputs expected, Including Target Date All HTI and HTI-CPRD-GOLD studies result in a scientific report structured along the lines of a scientific paper (e.g. Summary, Background, Methods, Results and Discussion). Interim tables of results (aggregated data with small number suppression in line with the HES analysis guide) may be circulated as interim results for discussion and appended to the study report. Further outputs include research publications in peer-reviewed journals and presentations at scientific conferences in addition to research included in Health Technology Assessments and Regulatory evidence. Researchers accessing HTI and HTI-CPRD-GOLD data are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included in the publically available IMS Global bibliography. IMS Health Ltd will present these results to participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd received updated HTI data on 21st Feb 2017 and received a further update on 29th March 2017. Having received the data, IMS Health Ltd have focussed on optimising the database design and database validation. This work will be completed by the end of April 2017 and the database will be available for researchers to access in May 2017. As all previously held data was deleted on receipt of the updated data no research is taking place during this period. The following outputs are expected for each of the research groups IMS Health Ltd engages with: 1. Participating NHS Trusts. IMS Health Ltd.’s research team is working with NHS Trusts to assist in the production of information that will impact on improving patient care. IMS Health Ltd have conducted interviews among chief pharmacists from participating hospitals and is engaging with UK clinical pharmacists association to determine their priorities. At the time of making this application, a shortlist of questions that would be of value to them has been drawn up. An analytical team has been put in place specifically for the purpose of answering these questions and generating a report. The report delivered to each trust will contain aggregated, small number suppressed data across all trusts and will also contain trust-specific outputs which will only be shared with that trust. These findings will be sent to Chief Pharmacists and Research departments at participating hospital Trusts. IMS Health Ltd will also hold an annual research day for hospital trusts and other healthcare stakeholders to make people aware of the types of research that the HTI database has been used for and present the findings from the hospital trust specific studies. The 2017 HTI annual research day will be held in October 2017. Over time IMS Health Ltd expect the annual research day to be a forum for hospital trusts and arm’s length bodies to discuss the type of questions they want to answer so IMS Health Ltd can produce content that is relevant and directly benefits healthcare. 2. Regulatory Authorities and Arm’s Length Bodies (ALBs) involved in Health and Social Care. The specific outputs expected for regulatory authorities include drug safety studies, signal detection and evaluation of adverse drug reactions of hospital prescribed therapies. ALBs such as NICE, NHS Digital and NHS England will be able to access the pseudonymised, non-sensitive record level database for conducting health technology assessments, monitoring adherence to guidance and to inform policy decisions. 3. Medical researchers from academia. Access to pseudonymised, non-sensitive record level database or delivery of aggregated tables for generation of research publications in peer-reviewed journals and presentations in scientific conferences. Information to be included in health technology assessments and evidence for regulators. 4. Pharmaceutical companies. Provision of aggregated, small number suppressed tables to answer research questions in areas of: Advanced statistical analysis • epidemiology • natural history of disease • health economics and outcomes research • drug exposures • Drug safety monitoring • pharmacovigilance Where possible, the outputs from these studies will be published in peer-reviewed journals and presented at scientific conferences, included in Health Technology Assessments or delivered to regulators. Pharmaceutical companies are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day. Pharmaceutical companies will not have direct access to the pseudonymised record level data. ***Specific study outputs (since last application)*** Due to the time needed to load and validate the HTI database and gain ISEAC approval for studies, IMS Health Ltd have not completed any new studies since receiving the updated data in March 2017. One study was completed on previously held data whilst IMS Health Ltd’s DSA was being renewed.	Expected measurable benefits to Health and/or Social care included Target date Applications for access to HTI and HTI-CPRD-GOLD require researchers, internal to IMS Health Ltd. and external researchers operating under honorary contract, to articulate the public health benefits of their research. Trusts: HTI will help hospital trusts understand the use of and outcomes associated with hospital prescribed medicines. This will enable Trusts to undertake evidence based decisions on access to high cost drugs, support treatment policies, compare provision and outcomes with other Trusts and monitor patient outcomes. IMS Health Ltd will conduct studies on behalf of trusts and results from these studies will be shared as part of an annual research report and also at an annual research day which will be attended by participating trusts and other healthcare stakeholders. ALBs: HTI will be used to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. Academia: HTI will be used to undertake independent research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness of treatment. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. Pharma: HTI will be used to answer focused, scientific research questions with clear health and social care benefits. Such studies will include epidemiology, natural history of disease and health outcomes research. These studies will add to the body of research evidence used for drug development and the results of these studies can support optimal allocation of finite NHS resources. All studies will be published or a blinded version of results will be made available on IMS Global Bibliography meaning findings and information from the studies will be available to the healthcare and medical research community. In addition, pharmaceutical companies will conduct pharmacovigilance studies using HTI data. HTI data enables pharmaceutical companies to fulfil their regulatory requirements and keeps patients safe through identification and evaluations of adverse drug reaction. 1. Patient safety Adverse drug reactions (ADRs) create a burden for the NHS (Ref 3) and are an important cause of mortality amongst hospitalised patients. HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. There is an unmet need for this type of data as a study found that 50 % of newly licensed drugs are now solely prescribed in secondary care and therefore could not be monitored in widely used primary care databases (Ref 4). IMS Health Ltd has conducted two important safety studies in the HTI database and intends to carry out more. In one study, IMS Health Ltd were engaged by a drug’s marketing authorisation holder to conduct a three year post-authorisation safety study (PASS), a requirement for the marketing approval for this drug set out by the European Medicines Agency (EMA) on patients in England. The EMA requested the use of the drug be monitored using HTI. The indication associated with use of the drug was extracted from the database and the site of administration was determined as off label usage of this formulation in contra- indicated sites has been shown to cause significant harm. In the case of off-label usage then the drug manufacturer will need to update their risk management plan to prevent this from happening in the future and to prevent patient harm. This should be of significant interest to the NHS due to implication for preventable harm and the potential for litigation. A second study looked at the use of a marketed medicine which is known to have harmful effects in specific subpopulations of cancer patients. The regulator requested monitoring of exposure to the drug within these populations. This protects patients from receiving drugs which are contraindicated for them. The results of the study were submitted to the European Medicines Agency in support of a risk management plan (RMP) which helped to characterise the overall benefit risk profile the drug and ensures that it is used as safely as possible. Safety information which is included in the summary of product characteristics and on the drug’s package leaflet in based on the RMP so findings and guidance from this study are directly available to healthcare professionals and patients. Using HTI for safety studies allows quick analysis as the data already exists. If this database was not available, the two safety studies described would have taken months rather than days if conducted by other methods and only measured a smaller number of patients. Using a larger sample of patients ensures that studies are robust and enables the detection of rare events. 2. IMS Health Ltd have performed a number of studies on healthcare resource utilisation within patients prescribed highcost drugs Autoimmune conditions are complicated to manage and result in debilitating conditions for patients. Recent immunomodulating therapies such as anti TNF based drugs (all prescribed in secondary care) have been shown to provide considerable benefit to patients with a reduction in morbidity and improved quality of life. However, these drugs are expensive and the control and use within guidelines is important for NHS trusts with implications for those involved with commissioning fully funded pathways. IMS Health Ltd has conducted a series of epidemiological studies in this therapy area to determine the dosing patterns, the indications for which the drugs are prescribed and the patient populations within which they are used. It has been shown that high cost drugs (anti-TNFs and biologics) are used more frequently in routine clinical practice than anticipated. This creates an additional cost burden to the NHS then planned for. Using HTI data IMS Health Ltd showed that inflammatory bowel disease patients treated with high cost drugs showed differences in the rates of hospitalisation and surgical interventions between different agents (Ref 1). This information can be used to identify patient groups that would benefit most from these high-cost drugs and allow resources to be allocated accordingly. This piece of work has been disseminated at one of the leading European conferences, the United European Gastroenterology Week (UEG). Attendees at the UEG include leading specialists across gastroenterology making it a key opportunity for knowledge sharing across the gastroenterology community. It has also been published via an open access journal PLOS One this means that NHS staff are able to access this for free via a standard literature search for evidence meaning there is no paywall standing in the way of health professionals accessing this material. 3. Probability of hospitalisation Intravenous iron therapy is not considered as first line treatment of iron deficiency anaemia in the majority of patients. IMS Health Ltd conducted an epidemiological study in collaboration with a pharmaceutical company to show that the 30- day readmission rates among those patients treated with IV were significantly lower than those treated with oral therapies (Ref 2). Readmission to hospital is distressing for patients but is also an inefficient use of NHS resources. 30 day readmission rate is a key quality metric that is used to evaluate NHS Trusts. This study was presented at the Digestive Disorders Federation's annual scientific meeting and its inclusion was decided by a panel of gastrointestinal experts. 4. Adherence to medication It is well known that nonadherence to medications can result in worsening clinical outcomes, including rehospitalisation, exacerbation of chronic medical conditions, increased healthcare costs and death [ref 5]. IMS Health Ltd and the CPRD conducted a feasibility study to investigate the % of patients who continued treatment of anticoagulant therapy in primary care within 90 days of discharge from hospital. Anticoagulants are effective for prevention of strokes and heart attacks but adherence to medication is crucial for maximising treatment benefits. The HTI-CPRD data showed that further anticoagulation therapy was continued in primary care in over half of hospitalisations discharged with these medications. This study was presented as a poster at the International Conference of Pharmacoepidemiology (ICPE) in 2016 [ref 6]. Ref 1. Dose Escalation and Healthcare Resource Use among Ulcerative Colitis Patients Treated with Adalimumab in English Hospitals: An Analysis of Real-World Data Christopher M. Black1, Eric Yu2, Eilish McCann3, Sumesh Kachroo1* 1 Merck &Co, Inc., Kenilworth, United States of America, 2 IMS Health Ltd, London, United Kingdom, 3 Merck Sharp & Dohme Ltd, Hoddesdon, United Kingdom http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149692 Ref 2. ASSOCIATION OF ORAL AND INTRAVENOUS IRON WITH THE PROBABILITY OF HOSPITALIZATION IN ENGLAND S.Keshav 1, C. Chapman 2, S. Tomkins 3,*, L. Mills 4, B. Jackson 41 Translational Gastroenterology Unit, John Radcliffe Hospital and University of Oxford, Oxford, 2 West Middlesex University Hospital, Isleworth, 3Real World Evidence, IMS Health, London, 4Vifor Pharma, Bagshot, United Kingdom http://gut.bmj.com/content/64/Suppl_1/A18.2 Ref 3. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients Munir Pirmohamed, professor of clinical pharmacology,1 Sally James, research pharmacist,3 Shaun Meakin, research nurse,2 Chris Green, senior pharmacist,2 Andrew K Scott, consultant in care of the elderly,3 Thomas J Walley, professor of clinical pharmacology,1 Keith Farrar, chief pharmacist,3 B Kevin Park, professor of pharmacology,1 and Alasdair M Breckenridge, professor of clinical pharmacology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC443443/ Ref 4. S. Cederholm, G. Hill, A. Asiimwe, A. Bate, F. Bhayat, G. Persson Brobert, T. Bergvall, D. Ansell, K. Star, and G. N. Norén. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.Drug Saf. 2015 Jan;38(1):87-100. doi: 10.1007/s40264-014-0251-y.PMID:25539877) http://www.who-umc.org/graphics/29625.pdf Ref 5. Robin Dimatteo M, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes A meta-analysis. Medical Care. 2002;40(9):794–811. [PubMed] Ref 6. Gallagher AM, Rockenschaub P, Tham R, Dattani H, Collier A, de Vries F, Williamsm T, Evaluating the utility of the CPRD GOLD-HTI linkage: anticoagulant prescribing at the GP practice compared to hospital dispensed medication at discharge date, ICPE 2016
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	IMS Health Ltd is an information and technology company serving the health care industry. IMS Health Ltd produces a longitudinal research database, Hospital Treatment Insights (HTI) and in collaboration with the CPRD produces HTI-CPRD-GOLD. This database contains unique information on diagnosis, treatment and drug usage across primary and secondary care in England and HTI is currently the only routinely collected population based database available for monitoring the safety of medicines used in the secondary care setting. NHS Digital acts as a Trusted Third Party and provides linkage services for both HTI and HTI-CPRD-GOLD datasets. The HTI data links record level pseudonymised, non-sensitive HES data to IMS Health Ltd’s unique database of hospital prescribing data. The HTI-CPRD-GOLD data is a subset of the HTI data and links record level HTI data to CPRD-GOLD data. Currently there are 6.4 million patients in HTI data and 650,000 patients in HTI-CPRD-GOLD. The HTI and HTI-CPRD-GOLD data are treated as separate databases for study purposes so a researcher must specify which data they are requesting access to. Researchers accessing these datasets will be substantive employees of IMS Health Ltd or external researchers who have signed an NHS Digital approved honorary contract with IMS Health Ltd. 1. Data will be used to undertake a programme of research studies, in two main areas, aimed at promoting public health which will include: Advanced statistical analysis • Epidemiology • natural history of disease • health economics and outcomes research • drug exposures Drug safety monitoring • monitoring of adverse events for newly licenced drugs prescribed in secondary care • pharmacovigilance In all cases, purposes are restricted to those for the provision of health care or adult social care, or the promotion of health. Access to the data will be as follows: 1. Regulatory Authorities. Regulators such as the Medicines and Healthcare Products Regulatory Agency (MHRA) and the European Medicines Agency (EMA) will investigate potential adverse drug reactions (ADR) in marketed products. When new ADRs are discovered regulators can recommend actions to limit harm to patients exposed to the products. Responsiveness is key as products are already marketed meaning that further events could occur immediately. Therefore it is crucial to have data, like HTI and HTI-CPRD-GOLD, readily available that will allow direct investigation of ADR or evaluation of the likely public health impact should the potential ADR be established as a real effect. Examples of current studies: IMS Health Ltd is in discussions with researchers who have a special interest in drug safety with a view to conducting a proof of concept drug safety study in HTI. Regulators, such as the MHRA and EMA, have also expressed an interest in using HTI data in their drug safety activities. They currently have access to data sources that collect information on drug exposure and clinical events in primary care. However, these regulators are also responsible for pharmacovigilance of products that are only used in a hospital setting. Currently they have no directly accessible data on the use of these drugs and clinical events in patients exposed to the products except those supplied via spontaneous reporting systems. The latter are frequently the source of signals regarding potential ADRs and hence have limited use in further investigation of the signal. For this reason regulators are in need of information on drug use in a hospital setting. Thus access to HTI data has an important role in improving the use of medicines used in hospitals and could have a significant impact on public health. 2. Arm’s Length Bodies (ALBs) involved in Health and Social Care. ALBs, such as NICE, can use HTI to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. HTI will allow the study of key performance measures used by the NHS, in association with pharmaceutical treatments in the following areas: • Uptake and utilisation of new and existing therapies • Medical vs surgical treatment rates associated with specific pharmaceutical treatments • Readmission rates associated with specific pharmaceutical treatments • Rates of elective vs non elective admissions in patients following different treatment regimens IMS Health Ltd is currently working with an academic researcher who sits on NICE’s appraisal committee to investigate equity of access to high cost drugs. This work would help to identify trends and variation in secondary care prescribing of high cost drugs across the country and by socioeconomic status and could be used to support national policy. 3. Medical researchers from academia and other types of organisation such as patient groups, charitable trusts and pharmaceutical companies. HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. The topic of sustainability will be a focus area for researchers, particularly as 94% of new molecular entities will be for specialist care delivered within the hospital setting. Current academic relationships: London School of Hygiene and Tropical Medicine (Faculty of Epidemiology and Population Health) ʹworking with the epidemiology group to study the cardiovascular outcomes of varying cancer treatments in survivors of breast cancer. University College London (Dept. of Public Health) ʹa validation study to determine the strengths and limitations of the data for antibiotic research also a second piece of work on babies diagnosed with Respiratory syncytial virus treated with Pavalizumab. Clinical Practice Research Datalink – A validation study looking at whether patients discharged from hospital continue their medication in primary care. A feasibility study looking at NOAC prescribing on discharge has already been conducted and a full study will be undertaken using the updated data. . There are controls in place to ensure secure access to the data: 1. Researcher access - the HTI and HTI-CPRD-GOLD databases contain pseudonymised data. All researchers accessing the data need to be substantive employees of IMS Health Ltd or must have an honorary contract with IMS Health Ltd in place. All researchers accessing these data undertake training and sign additional confidentiality agreements with IMS Health Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. IMS Health Ltd does not permit any access to pseudonymised patient record-level data from outside of the UK. 2. Strong internal governance process - researchers only access the HTI and HTI-CPRD-GOLD data for single-study research projects that have received approval from IMS’ Independent Scientific Ethical Advisory Committee (ISEAC) for HTI studies and the CPRD͛s Independent Scientific Advisory Committee (ISAC) for HTI-CPRD-GOLD studies. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol and statistical analysis plan (SAP). IMS Health Ltd maintains an access control register and all usage of the database against an ISEAC or ISAC approved protocol is logged and auditable. Where appropriate, researchers accessing HTI or HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included on the publically available IMS global bibliography. IMS Health Ltd will share these findings with participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd will not make the outputs of safety studies publically available to avoid generating undue public concern before guidance is issued by regulatory agencies. Data minimisation: IMS Health Ltd understand the importance of data minimisation and have taken steps to reduce the number of HES records requested. IMS is requesting access to the following data: • HES records, either in an HTI trust or a non-HTI Trust, that can be linked to a pharmacy record • All other HES records from HTI Trusts that can't be linked to a pharmacy record. These records will only be used for data validation purposes to compare the % of linkage across different trusts and therapy areas. These records will only be accessible to researchers for data validation purposes and will be used to indicate whether the data is high quality enough for medical research studies. Justification for historical data - IMS Health Ltd uses historical HES data (from 2005 where available) in HTI studies in order to identify diagnosis prior to patients receiving a drug, determine any co-morbid conditions and identify the date when a patient first had a secondary care visit for a particular diagnosis (index date). HTI has been used to conduct studies on drug treatment for chronic conditions including psoriasis, rheumatoid arthritis, multiple sclerosis and ulcerative colitis. Patients with chronic diseases have these conditions for life so it is important to have the maximum number of years of back data in order to conduct studies rigorously. When researchers conduct studies using HTI they need to establish the index date of a patient at the beginning of treatment or diagnosis in order to determine progress and treatment efficacy over a follow up period. They also need to understand if patients have a history of serious comorbid conditions e.g. if a patient was hospitalised 10 years ago for a stroke then this needs to be taken into account. By answering these questions researchers are able to build cohorts for studies with the right type of characteristics. If historical HES data was not provided then researchers could miss important events which would then not be adjusted for in study results. In addition the historical data will be used to detect rare, delayed adverse events. Researchers from regulatory agencies need access to historical HES data in the HTI database in order to monitor drug safety, particularly of rare and delayed adverse events which may take many years to develop. Further scientific need for the historical data – • Historic data is required to support Advanced Statistical Analysis projects and safety studies, as historical data allows robust analysis of trends over time. • Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” • Historical data is needed for patients with chronic conditions to understand disease progression and can be used to investigate how the usage of different treatments impacts the typical time of disease progression • Historical data is needed to understand previous and co-morbid conditions in order to adjust for these in the research study outcome. For example a trust could be incorrectly identified as having poor outcomes or performance when they are in fact treating sicker or higher risk patients e.g. patients with previous cardiovascular and stroke events. This information in also needed to ensure the research outcome is correctly interpreted so that the medical professionals are able to provide the most appropriate care for patients e.g. a medication may be considered to have higher risk profile in a certain patient population • Historical data provides extended longitudinal coverage to allow researchers to look at delayed adverse events or outcomes which have a long latency period from the time of exposure to manifestation. Further justification for the historical data requirement - 1. HTI is unique in that it enables the study of hospital only medicines in a real world setting. This is very important because the patients receiving these medicines don’t always reflect those that the medicines are tested in. Many HTI studies are observational studies of the way in which drugs are used i.e. characterising the patients that receive them and then following those patients to see whether or not they benefit from the drug(s) in question. These studies are conducted over a specified study period i.e. a time frame over which the observation takes place. Setting the time period requires a starting point or ‘index’ date. This is usually the date at which a diagnosis has been made for which the drug being studied is a treatment. For chronic conditions diagnoses are made early in the patients’ history and the historic HES data enables an accurate index date to be set. Without the historic data, important information around the diagnosis of the disease would be missed. 2. HTI has been used to study the use of biologics in the treatment of autoimmune diseases such as ulcerative colitis and rheumatoid arthritis. These are expensive hospital only drugs and are not the first line of therapy for these diseases. The studies we have conducted have looked at healthcare resource use prior to the administration of these drugs i.e. the treatment pathway leading up to the administration of these drugs. Without the historic HES data, these studies would not be possible. 3. One very important use of HTI is in drug safety studies. These studies can sometimes involve identifying potential ‘signals’ or side effects of a drug. For these studies it is vital to access as much of a patient’s past medical history as possible. For example, some new drugs may have the potential to cause cardiovascular side effects, in order to evaluate these it would be important to know if a patients has suffered from cardiovascular problems in the past and to include as much detail as possible around those events into the analysis. 4. Some studies look at the difference in outcomes associated with different treatment options for a particular disease i.e. what are the characteristics of patients that respond better to one treatment over another. These studies are important because they can help doctors decide which treatment option would be more suitable for a patient. In order to conduct these studies with rigor and as much accuracy as possible, it is important to take into account every aspect the patient’s health state from their history because the past medical history affects the decisions that doctors take for current and future drug therapy. 5. Large databases like HTI are often used to conduct observational epidemiological studies. The starting point of these studies requires building a cohort of study patients based on strict inclusion and exclusion criteria. Without access to the past medical history, it is difficult to ensure that the patients included in the study are the ‘right’ patients and the study results could be biased. 6. For the study of chronic and rare diseases and delayed side effects of medicines, a long past medical history is important because the lead up to diagnosis of such conditions can be complex and lengthy. 7. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” 8. Past medical history often includes risk factors, co-morbidities and confounders that are important to adjust for when analysing data in order to ensure that conclusions drawn from the research are generalizable and robust.	Processing Activities Data flow Each month, participating trusts provide three files to NHS Digital. The 'TRUSTED' file contains patient identifiable hospital pharmacy issues data, which is used for the subsequent linkage to HES. The 'ISSUES' file is a non-identifiable version of the TRUSTED file which NHS Digital provides onward to IMS Health Ltd, and also checks against the TRUSTED file to ensure the payload data in the two files are consistent. A third data definition file ͚’DEFS͛’ is also provided to NHS Digital which is forwarded to IMS Health Ltd. The definitions file contains details of the drug such as name and the ward that issued it, does not contain identifiable data. The ISSUES and DEFS files (both of which contain no identifiable data) are received by IMS Health Ltd for their hospital pharmacy audit work, which is outside the scope of this agreement. Hospital prescribing and HES data are linked by NHS Digital and data are pseudonymised before being passed on to IMS Health Ltd on a quarterly basis. Once received, these data are downloaded via SFTP to a secure server within an ISO27001 accredited environment. Security measures include: • Access authentication • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions Remote Access to IMS ISO27001 compliant environment: Researchers access the IS027001 environment remotely via a secure portal. Researchers are able to query the data within this environment and create patient cohorts for further study. Data cannot be copied from the secure environment and usage of the secure environment is auditable. Researchers can export aggregated, small number suppressed data from the secure environment and a record of all exports is kept for monitoring and audit purposes. External Researcher access: Access to the data is only permitted for substantive IMS Health Ltd employees and researchers working under honorary contract to IMS Health Ltd. The honorary contract in use has previously been approved by DAAG and obligates the researcher to adhere to the terms and conditions of IMS Health Ltd.’s DSA. Honorary contractors are subject to the same access controls as substantive IMS Health Ltd employees. They are provided with a username and password and access the data through the secure portal. If an honorary contractor is accessing data from any location apart from the IMS Health Ltd office they will be required to provide details of the processing location in their honorary contract. IMS Health Ltd validates that the data processing location listed has appropriate security measures in place such as ISO27001 or IG Toolkit before access to the data is granted. Access to the data is not permitted from outside the UK. IMS Health Ltd reserves the right to undertake an audit of the honorary contractor at any time to ensure that appropriate security measures are in place and that all terms of the agreement are being abided by (such as agreed processing location). IMS Health Limited are not permitted to approve or otherwise authorise the use of the data supplied by NHS Digital (or predecessor organisations) for any additional purposes other than those described in the Purpose/Methods/Outputs section. No new projects or purposes are permitted under this agreement. Data cannot be used for solely commercial purposes as per the requirements of the Care Act 2014 Clause for preventing commercial use IMS Health Ltd will to include the wording below in contracts with external organisations. “NHS Digital provides IMS Health Ltd with Hospital Episode Statistics in accordance with the amended section 261 of the Health and Social Care Act 2012 i.e. “if it considers that disseminating the information would be for the purposes of— a) the provision of health care or adult social care, or b) the promotion of health.” Therefore, the CLIENT is not permitted to use the HES analytical services provided by IMS Health Ltd to do the following: • analysis for insurance companies • analysis to identify or communicate with directly with patients (or groups thereof) for purposes other than their direct care • analysis to identify or communicate with clinicians for solely sales or marketing • analysis to quantify the impact of marketing campaigns • analysis to inform salesforce structure or to understand salesforce performance” Data sharing with third party organisations under sub-licence is not permitted under this agreement. Record level data or aggregate data containing small numbers will not be shared within the IMS group of organisations.	5c. Specific Outputs expected, Including Target Date All HTI and HTI-CPRD-GOLD studies result in a scientific report structured along the lines of a scientific paper (e.g. Summary, Background, Methods, Results and Discussion). Interim tables of results (aggregated data with small number suppression in line with the HES analysis guide) may be circulated as interim results for discussion and appended to the study report. Further outputs include research publications in peer-reviewed journals and presentations at scientific conferences in addition to research included in Health Technology Assessments and Regulatory evidence. Researchers accessing HTI and HTI-CPRD-GOLD data are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included in the publically available IMS Global bibliography. IMS Health Ltd will present these results to participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd received updated HTI data on 21st Feb 2017 and received a further update on 29th March 2017. Having received the data, IMS Health Ltd have focussed on optimising the database design and database validation. This work will be completed by the end of April 2017 and the database will be available for researchers to access in May 2017. As all previously held data was deleted on receipt of the updated data no research is taking place during this period. The following outputs are expected for each of the research groups IMS Health Ltd engages with: 1. Participating NHS Trusts. IMS Health Ltd.’s research team is working with NHS Trusts to assist in the production of information that will impact on improving patient care. IMS Health Ltd have conducted interviews among chief pharmacists from participating hospitals and is engaging with UK clinical pharmacists association to determine their priorities. At the time of making this application, a shortlist of questions that would be of value to them has been drawn up. An analytical team has been put in place specifically for the purpose of answering these questions and generating a report. The report delivered to each trust will contain aggregated, small number suppressed data across all trusts and will also contain trust-specific outputs which will only be shared with that trust. These findings will be sent to Chief Pharmacists and Research departments at participating hospital Trusts. IMS Health Ltd will also hold an annual research day for hospital trusts and other healthcare stakeholders to make people aware of the types of research that the HTI database has been used for and present the findings from the hospital trust specific studies. The 2017 HTI annual research day will be held in October 2017. Over time IMS Health Ltd expect the annual research day to be a forum for hospital trusts and arm’s length bodies to discuss the type of questions they want to answer so IMS Health Ltd can produce content that is relevant and directly benefits healthcare. 2. Regulatory Authorities and Arm’s Length Bodies (ALBs) involved in Health and Social Care. The specific outputs expected for regulatory authorities include drug safety studies, signal detection and evaluation of adverse drug reactions of hospital prescribed therapies. ALBs such as NICE, NHS Digital and NHS England will be able to access the pseudonymised, non-sensitive record level database for conducting health technology assessments, monitoring adherence to guidance and to inform policy decisions. 3. Medical researchers from academia. Access to pseudonymised, non-sensitive record level database or delivery of aggregated tables for generation of research publications in peer-reviewed journals and presentations in scientific conferences. Information to be included in health technology assessments and evidence for regulators. 4. Pharmaceutical companies. Provision of aggregated, small number suppressed tables to answer research questions in areas of: Advanced statistical analysis • epidemiology • natural history of disease • health economics and outcomes research • drug exposures • Drug safety monitoring • pharmacovigilance Where possible, the outputs from these studies will be published in peer-reviewed journals and presented at scientific conferences, included in Health Technology Assessments or delivered to regulators. Pharmaceutical companies are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day. Pharmaceutical companies will not have direct access to the pseudonymised record level data. ***Specific study outputs (since last application)*** Due to the time needed to load and validate the HTI database and gain ISEAC approval for studies, IMS Health Ltd have not completed any new studies since receiving the updated data in March 2017. One study was completed on previously held data whilst IMS Health Ltd’s DSA was being renewed.	Expected measurable benefits to Health and/or Social care included Target date Applications for access to HTI and HTI-CPRD-GOLD require researchers, internal to IMS Health Ltd. and external researchers operating under honorary contract, to articulate the public health benefits of their research. Trusts: HTI will help hospital trusts understand the use of and outcomes associated with hospital prescribed medicines. This will enable Trusts to undertake evidence based decisions on access to high cost drugs, support treatment policies, compare provision and outcomes with other Trusts and monitor patient outcomes. IMS Health Ltd will conduct studies on behalf of trusts and results from these studies will be shared as part of an annual research report and also at an annual research day which will be attended by participating trusts and other healthcare stakeholders. ALBs: HTI will be used to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. Academia: HTI will be used to undertake independent research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness of treatment. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. Pharma: HTI will be used to answer focused, scientific research questions with clear health and social care benefits. Such studies will include epidemiology, natural history of disease and health outcomes research. These studies will add to the body of research evidence used for drug development and the results of these studies can support optimal allocation of finite NHS resources. All studies will be published or a blinded version of results will be made available on IMS Global Bibliography meaning findings and information from the studies will be available to the healthcare and medical research community. In addition, pharmaceutical companies will conduct pharmacovigilance studies using HTI data. HTI data enables pharmaceutical companies to fulfil their regulatory requirements and keeps patients safe through identification and evaluations of adverse drug reaction. 1. Patient safety Adverse drug reactions (ADRs) create a burden for the NHS (Ref 3) and are an important cause of mortality amongst hospitalised patients. HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. There is an unmet need for this type of data as a study found that 50 % of newly licensed drugs are now solely prescribed in secondary care and therefore could not be monitored in widely used primary care databases (Ref 4). IMS Health Ltd has conducted two important safety studies in the HTI database and intends to carry out more. In one study, IMS Health Ltd were engaged by a drug’s marketing authorisation holder to conduct a three year post-authorisation safety study (PASS), a requirement for the marketing approval for this drug set out by the European Medicines Agency (EMA) on patients in England. The EMA requested the use of the drug be monitored using HTI. The indication associated with use of the drug was extracted from the database and the site of administration was determined as off label usage of this formulation in contra- indicated sites has been shown to cause significant harm. In the case of off-label usage then the drug manufacturer will need to update their risk management plan to prevent this from happening in the future and to prevent patient harm. This should be of significant interest to the NHS due to implication for preventable harm and the potential for litigation. A second study looked at the use of a marketed medicine which is known to have harmful effects in specific subpopulations of cancer patients. The regulator requested monitoring of exposure to the drug within these populations. This protects patients from receiving drugs which are contraindicated for them. The results of the study were submitted to the European Medicines Agency in support of a risk management plan (RMP) which helped to characterise the overall benefit risk profile the drug and ensures that it is used as safely as possible. Safety information which is included in the summary of product characteristics and on the drug’s package leaflet in based on the RMP so findings and guidance from this study are directly available to healthcare professionals and patients. Using HTI for safety studies allows quick analysis as the data already exists. If this database was not available, the two safety studies described would have taken months rather than days if conducted by other methods and only measured a smaller number of patients. Using a larger sample of patients ensures that studies are robust and enables the detection of rare events. 2. IMS Health Ltd have performed a number of studies on healthcare resource utilisation within patients prescribed highcost drugs Autoimmune conditions are complicated to manage and result in debilitating conditions for patients. Recent immunomodulating therapies such as anti TNF based drugs (all prescribed in secondary care) have been shown to provide considerable benefit to patients with a reduction in morbidity and improved quality of life. However, these drugs are expensive and the control and use within guidelines is important for NHS trusts with implications for those involved with commissioning fully funded pathways. IMS Health Ltd has conducted a series of epidemiological studies in this therapy area to determine the dosing patterns, the indications for which the drugs are prescribed and the patient populations within which they are used. It has been shown that high cost drugs (anti-TNFs and biologics) are used more frequently in routine clinical practice than anticipated. This creates an additional cost burden to the NHS then planned for. Using HTI data IMS Health Ltd showed that inflammatory bowel disease patients treated with high cost drugs showed differences in the rates of hospitalisation and surgical interventions between different agents (Ref 1). This information can be used to identify patient groups that would benefit most from these high-cost drugs and allow resources to be allocated accordingly. This piece of work has been disseminated at one of the leading European conferences, the United European Gastroenterology Week (UEG). Attendees at the UEG include leading specialists across gastroenterology making it a key opportunity for knowledge sharing across the gastroenterology community. It has also been published via an open access journal PLOS One this means that NHS staff are able to access this for free via a standard literature search for evidence meaning there is no paywall standing in the way of health professionals accessing this material. 3. Probability of hospitalisation Intravenous iron therapy is not considered as first line treatment of iron deficiency anaemia in the majority of patients. IMS Health Ltd conducted an epidemiological study in collaboration with a pharmaceutical company to show that the 30- day readmission rates among those patients treated with IV were significantly lower than those treated with oral therapies (Ref 2). Readmission to hospital is distressing for patients but is also an inefficient use of NHS resources. 30 day readmission rate is a key quality metric that is used to evaluate NHS Trusts. This study was presented at the Digestive Disorders Federation's annual scientific meeting and its inclusion was decided by a panel of gastrointestinal experts. 4. Adherence to medication It is well known that nonadherence to medications can result in worsening clinical outcomes, including rehospitalisation, exacerbation of chronic medical conditions, increased healthcare costs and death [ref 5]. IMS Health Ltd and the CPRD conducted a feasibility study to investigate the % of patients who continued treatment of anticoagulant therapy in primary care within 90 days of discharge from hospital. Anticoagulants are effective for prevention of strokes and heart attacks but adherence to medication is crucial for maximising treatment benefits. The HTI-CPRD data showed that further anticoagulation therapy was continued in primary care in over half of hospitalisations discharged with these medications. This study was presented as a poster at the International Conference of Pharmacoepidemiology (ICPE) in 2016 [ref 6]. Ref 1. Dose Escalation and Healthcare Resource Use among Ulcerative Colitis Patients Treated with Adalimumab in English Hospitals: An Analysis of Real-World Data Christopher M. Black1, Eric Yu2, Eilish McCann3, Sumesh Kachroo1* 1 Merck &Co, Inc., Kenilworth, United States of America, 2 IMS Health Ltd, London, United Kingdom, 3 Merck Sharp & Dohme Ltd, Hoddesdon, United Kingdom http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149692 Ref 2. ASSOCIATION OF ORAL AND INTRAVENOUS IRON WITH THE PROBABILITY OF HOSPITALIZATION IN ENGLAND S.Keshav 1, C. Chapman 2, S. Tomkins 3,*, L. Mills 4, B. Jackson 41 Translational Gastroenterology Unit, John Radcliffe Hospital and University of Oxford, Oxford, 2 West Middlesex University Hospital, Isleworth, 3Real World Evidence, IMS Health, London, 4Vifor Pharma, Bagshot, United Kingdom http://gut.bmj.com/content/64/Suppl_1/A18.2 Ref 3. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients Munir Pirmohamed, professor of clinical pharmacology,1 Sally James, research pharmacist,3 Shaun Meakin, research nurse,2 Chris Green, senior pharmacist,2 Andrew K Scott, consultant in care of the elderly,3 Thomas J Walley, professor of clinical pharmacology,1 Keith Farrar, chief pharmacist,3 B Kevin Park, professor of pharmacology,1 and Alasdair M Breckenridge, professor of clinical pharmacology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC443443/ Ref 4. S. Cederholm, G. Hill, A. Asiimwe, A. Bate, F. Bhayat, G. Persson Brobert, T. Bergvall, D. Ansell, K. Star, and G. N. Norén. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.Drug Saf. 2015 Jan;38(1):87-100. doi: 10.1007/s40264-014-0251-y.PMID:25539877) http://www.who-umc.org/graphics/29625.pdf Ref 5. Robin Dimatteo M, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes A meta-analysis. Medical Care. 2002;40(9):794–811. [PubMed] Ref 6. Gallagher AM, Rockenschaub P, Tham R, Dattani H, Collier A, de Vries F, Williamsm T, Evaluating the utility of the CPRD GOLD-HTI linkage: anticoagulant prescribing at the GP practice compared to hospital dispensed medication at discharge date, ICPE 2016
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	IMS Health Ltd is an information and technology company serving the health care industry. IMS Health Ltd produces a longitudinal research database, Hospital Treatment Insights (HTI) and in collaboration with the CPRD produces HTI-CPRD-GOLD. This database contains unique information on diagnosis, treatment and drug usage across primary and secondary care in England and HTI is currently the only routinely collected population based database available for monitoring the safety of medicines used in the secondary care setting. NHS Digital acts as a Trusted Third Party and provides linkage services for both HTI and HTI-CPRD-GOLD datasets. The HTI data links record level pseudonymised, non-sensitive HES data to IMS Health Ltd’s unique database of hospital prescribing data. The HTI-CPRD-GOLD data is a subset of the HTI data and links record level HTI data to CPRD-GOLD data. Currently there are 6.4 million patients in HTI data and 650,000 patients in HTI-CPRD-GOLD. The HTI and HTI-CPRD-GOLD data are treated as separate databases for study purposes so a researcher must specify which data they are requesting access to. Researchers accessing these datasets will be substantive employees of IMS Health Ltd or external researchers who have signed an NHS Digital approved honorary contract with IMS Health Ltd. 1. Data will be used to undertake a programme of research studies, in two main areas, aimed at promoting public health which will include: Advanced statistical analysis • Epidemiology • natural history of disease • health economics and outcomes research • drug exposures Drug safety monitoring • monitoring of adverse events for newly licenced drugs prescribed in secondary care • pharmacovigilance In all cases, purposes are restricted to those for the provision of health care or adult social care, or the promotion of health. Access to the data will be as follows: 1. Regulatory Authorities. Regulators such as the Medicines and Healthcare Products Regulatory Agency (MHRA) and the European Medicines Agency (EMA) will investigate potential adverse drug reactions (ADR) in marketed products. When new ADRs are discovered regulators can recommend actions to limit harm to patients exposed to the products. Responsiveness is key as products are already marketed meaning that further events could occur immediately. Therefore it is crucial to have data, like HTI and HTI-CPRD-GOLD, readily available that will allow direct investigation of ADR or evaluation of the likely public health impact should the potential ADR be established as a real effect. Examples of current studies: IMS Health Ltd is in discussions with researchers who have a special interest in drug safety with a view to conducting a proof of concept drug safety study in HTI. Regulators, such as the MHRA and EMA, have also expressed an interest in using HTI data in their drug safety activities. They currently have access to data sources that collect information on drug exposure and clinical events in primary care. However, these regulators are also responsible for pharmacovigilance of products that are only used in a hospital setting. Currently they have no directly accessible data on the use of these drugs and clinical events in patients exposed to the products except those supplied via spontaneous reporting systems. The latter are frequently the source of signals regarding potential ADRs and hence have limited use in further investigation of the signal. For this reason regulators are in need of information on drug use in a hospital setting. Thus access to HTI data has an important role in improving the use of medicines used in hospitals and could have a significant impact on public health. 2. Arm’s Length Bodies (ALBs) involved in Health and Social Care. ALBs, such as NICE, can use HTI to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. HTI will allow the study of key performance measures used by the NHS, in association with pharmaceutical treatments in the following areas: • Uptake and utilisation of new and existing therapies • Medical vs surgical treatment rates associated with specific pharmaceutical treatments • Readmission rates associated with specific pharmaceutical treatments • Rates of elective vs non elective admissions in patients following different treatment regimens IMS Health Ltd is currently working with an academic researcher who sits on NICE’s appraisal committee to investigate equity of access to high cost drugs. This work would help to identify trends and variation in secondary care prescribing of high cost drugs across the country and by socioeconomic status and could be used to support national policy. 3. Medical researchers from academia and other types of organisation such as patient groups, charitable trusts and pharmaceutical companies. HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. The topic of sustainability will be a focus area for researchers, particularly as 94% of new molecular entities will be for specialist care delivered within the hospital setting. Current academic relationships: London School of Hygiene and Tropical Medicine (Faculty of Epidemiology and Population Health) ʹworking with the epidemiology group to study the cardiovascular outcomes of varying cancer treatments in survivors of breast cancer. University College London (Dept. of Public Health) ʹa validation study to determine the strengths and limitations of the data for antibiotic research also a second piece of work on babies diagnosed with Respiratory syncytial virus treated with Pavalizumab. Clinical Practice Research Datalink – A validation study looking at whether patients discharged from hospital continue their medication in primary care. A feasibility study looking at NOAC prescribing on discharge has already been conducted and a full study will be undertaken using the updated data. . There are controls in place to ensure secure access to the data: 1. Researcher access - the HTI and HTI-CPRD-GOLD databases contain pseudonymised data. All researchers accessing the data need to be substantive employees of IMS Health Ltd or must have an honorary contract with IMS Health Ltd in place. All researchers accessing these data undertake training and sign additional confidentiality agreements with IMS Health Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. IMS Health Ltd does not permit any access to pseudonymised patient record-level data from outside of the UK. 2. Strong internal governance process - researchers only access the HTI and HTI-CPRD-GOLD data for single-study research projects that have received approval from IMS’ Independent Scientific Ethical Advisory Committee (ISEAC) for HTI studies and the CPRD͛s Independent Scientific Advisory Committee (ISAC) for HTI-CPRD-GOLD studies. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol and statistical analysis plan (SAP). IMS Health Ltd maintains an access control register and all usage of the database against an ISEAC or ISAC approved protocol is logged and auditable. Where appropriate, researchers accessing HTI or HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included on the publically available IMS global bibliography. IMS Health Ltd will share these findings with participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd will not make the outputs of safety studies publically available to avoid generating undue public concern before guidance is issued by regulatory agencies. Data minimisation: IMS Health Ltd understand the importance of data minimisation and have taken steps to reduce the number of HES records requested. IMS is requesting access to the following data: • HES records, either in an HTI trust or a non-HTI Trust, that can be linked to a pharmacy record • All other HES records from HTI Trusts that can't be linked to a pharmacy record. These records will only be used for data validation purposes to compare the % of linkage across different trusts and therapy areas. These records will only be accessible to researchers for data validation purposes and will be used to indicate whether the data is high quality enough for medical research studies. Justification for historical data - IMS Health Ltd uses historical HES data (from 2005 where available) in HTI studies in order to identify diagnosis prior to patients receiving a drug, determine any co-morbid conditions and identify the date when a patient first had a secondary care visit for a particular diagnosis (index date). HTI has been used to conduct studies on drug treatment for chronic conditions including psoriasis, rheumatoid arthritis, multiple sclerosis and ulcerative colitis. Patients with chronic diseases have these conditions for life so it is important to have the maximum number of years of back data in order to conduct studies rigorously. When researchers conduct studies using HTI they need to establish the index date of a patient at the beginning of treatment or diagnosis in order to determine progress and treatment efficacy over a follow up period. They also need to understand if patients have a history of serious comorbid conditions e.g. if a patient was hospitalised 10 years ago for a stroke then this needs to be taken into account. By answering these questions researchers are able to build cohorts for studies with the right type of characteristics. If historical HES data was not provided then researchers could miss important events which would then not be adjusted for in study results. In addition the historical data will be used to detect rare, delayed adverse events. Researchers from regulatory agencies need access to historical HES data in the HTI database in order to monitor drug safety, particularly of rare and delayed adverse events which may take many years to develop. Further scientific need for the historical data – • Historic data is required to support Advanced Statistical Analysis projects and safety studies, as historical data allows robust analysis of trends over time. • Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” • Historical data is needed for patients with chronic conditions to understand disease progression and can be used to investigate how the usage of different treatments impacts the typical time of disease progression • Historical data is needed to understand previous and co-morbid conditions in order to adjust for these in the research study outcome. For example a trust could be incorrectly identified as having poor outcomes or performance when they are in fact treating sicker or higher risk patients e.g. patients with previous cardiovascular and stroke events. This information in also needed to ensure the research outcome is correctly interpreted so that the medical professionals are able to provide the most appropriate care for patients e.g. a medication may be considered to have higher risk profile in a certain patient population • Historical data provides extended longitudinal coverage to allow researchers to look at delayed adverse events or outcomes which have a long latency period from the time of exposure to manifestation. Further justification for the historical data requirement - 1. HTI is unique in that it enables the study of hospital only medicines in a real world setting. This is very important because the patients receiving these medicines don’t always reflect those that the medicines are tested in. Many HTI studies are observational studies of the way in which drugs are used i.e. characterising the patients that receive them and then following those patients to see whether or not they benefit from the drug(s) in question. These studies are conducted over a specified study period i.e. a time frame over which the observation takes place. Setting the time period requires a starting point or ‘index’ date. This is usually the date at which a diagnosis has been made for which the drug being studied is a treatment. For chronic conditions diagnoses are made early in the patients’ history and the historic HES data enables an accurate index date to be set. Without the historic data, important information around the diagnosis of the disease would be missed. 2. HTI has been used to study the use of biologics in the treatment of autoimmune diseases such as ulcerative colitis and rheumatoid arthritis. These are expensive hospital only drugs and are not the first line of therapy for these diseases. The studies we have conducted have looked at healthcare resource use prior to the administration of these drugs i.e. the treatment pathway leading up to the administration of these drugs. Without the historic HES data, these studies would not be possible. 3. One very important use of HTI is in drug safety studies. These studies can sometimes involve identifying potential ‘signals’ or side effects of a drug. For these studies it is vital to access as much of a patient’s past medical history as possible. For example, some new drugs may have the potential to cause cardiovascular side effects, in order to evaluate these it would be important to know if a patients has suffered from cardiovascular problems in the past and to include as much detail as possible around those events into the analysis. 4. Some studies look at the difference in outcomes associated with different treatment options for a particular disease i.e. what are the characteristics of patients that respond better to one treatment over another. These studies are important because they can help doctors decide which treatment option would be more suitable for a patient. In order to conduct these studies with rigor and as much accuracy as possible, it is important to take into account every aspect the patient’s health state from their history because the past medical history affects the decisions that doctors take for current and future drug therapy. 5. Large databases like HTI are often used to conduct observational epidemiological studies. The starting point of these studies requires building a cohort of study patients based on strict inclusion and exclusion criteria. Without access to the past medical history, it is difficult to ensure that the patients included in the study are the ‘right’ patients and the study results could be biased. 6. For the study of chronic and rare diseases and delayed side effects of medicines, a long past medical history is important because the lead up to diagnosis of such conditions can be complex and lengthy. 7. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” 8. Past medical history often includes risk factors, co-morbidities and confounders that are important to adjust for when analysing data in order to ensure that conclusions drawn from the research are generalizable and robust.	Processing Activities Data flow Each month, participating trusts provide three files to NHS Digital. The 'TRUSTED' file contains patient identifiable hospital pharmacy issues data, which is used for the subsequent linkage to HES. The 'ISSUES' file is a non-identifiable version of the TRUSTED file which NHS Digital provides onward to IMS Health Ltd, and also checks against the TRUSTED file to ensure the payload data in the two files are consistent. A third data definition file ͚’DEFS͛’ is also provided to NHS Digital which is forwarded to IMS Health Ltd. The definitions file contains details of the drug such as name and the ward that issued it, does not contain identifiable data. The ISSUES and DEFS files (both of which contain no identifiable data) are received by IMS Health Ltd for their hospital pharmacy audit work, which is outside the scope of this agreement. Hospital prescribing and HES data are linked by NHS Digital and data are pseudonymised before being passed on to IMS Health Ltd on a quarterly basis. Once received, these data are downloaded via SFTP to a secure server within an ISO27001 accredited environment. Security measures include: • Access authentication • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions Remote Access to IMS ISO27001 compliant environment: Researchers access the IS027001 environment remotely via a secure portal. Researchers are able to query the data within this environment and create patient cohorts for further study. Data cannot be copied from the secure environment and usage of the secure environment is auditable. Researchers can export aggregated, small number suppressed data from the secure environment and a record of all exports is kept for monitoring and audit purposes. External Researcher access: Access to the data is only permitted for substantive IMS Health Ltd employees and researchers working under honorary contract to IMS Health Ltd. The honorary contract in use has previously been approved by DAAG and obligates the researcher to adhere to the terms and conditions of IMS Health Ltd.’s DSA. Honorary contractors are subject to the same access controls as substantive IMS Health Ltd employees. They are provided with a username and password and access the data through the secure portal. If an honorary contractor is accessing data from any location apart from the IMS Health Ltd office they will be required to provide details of the processing location in their honorary contract. IMS Health Ltd validates that the data processing location listed has appropriate security measures in place such as ISO27001 or IG Toolkit before access to the data is granted. Access to the data is not permitted from outside the UK. IMS Health Ltd reserves the right to undertake an audit of the honorary contractor at any time to ensure that appropriate security measures are in place and that all terms of the agreement are being abided by (such as agreed processing location). IMS Health Limited are not permitted to approve or otherwise authorise the use of the data supplied by NHS Digital (or predecessor organisations) for any additional purposes other than those described in the Purpose/Methods/Outputs section. No new projects or purposes are permitted under this agreement. Data cannot be used for solely commercial purposes as per the requirements of the Care Act 2014 Clause for preventing commercial use IMS Health Ltd will to include the wording below in contracts with external organisations. “NHS Digital provides IMS Health Ltd with Hospital Episode Statistics in accordance with the amended section 261 of the Health and Social Care Act 2012 i.e. “if it considers that disseminating the information would be for the purposes of— a) the provision of health care or adult social care, or b) the promotion of health.” Therefore, the CLIENT is not permitted to use the HES analytical services provided by IMS Health Ltd to do the following: • analysis for insurance companies • analysis to identify or communicate with directly with patients (or groups thereof) for purposes other than their direct care • analysis to identify or communicate with clinicians for solely sales or marketing • analysis to quantify the impact of marketing campaigns • analysis to inform salesforce structure or to understand salesforce performance” Data sharing with third party organisations under sub-licence is not permitted under this agreement. Record level data or aggregate data containing small numbers will not be shared within the IMS group of organisations.	5c. Specific Outputs expected, Including Target Date All HTI and HTI-CPRD-GOLD studies result in a scientific report structured along the lines of a scientific paper (e.g. Summary, Background, Methods, Results and Discussion). Interim tables of results (aggregated data with small number suppression in line with the HES analysis guide) may be circulated as interim results for discussion and appended to the study report. Further outputs include research publications in peer-reviewed journals and presentations at scientific conferences in addition to research included in Health Technology Assessments and Regulatory evidence. Researchers accessing HTI and HTI-CPRD-GOLD data are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included in the publically available IMS Global bibliography. IMS Health Ltd will present these results to participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd received updated HTI data on 21st Feb 2017 and received a further update on 29th March 2017. Having received the data, IMS Health Ltd have focussed on optimising the database design and database validation. This work will be completed by the end of April 2017 and the database will be available for researchers to access in May 2017. As all previously held data was deleted on receipt of the updated data no research is taking place during this period. The following outputs are expected for each of the research groups IMS Health Ltd engages with: 1. Participating NHS Trusts. IMS Health Ltd.’s research team is working with NHS Trusts to assist in the production of information that will impact on improving patient care. IMS Health Ltd have conducted interviews among chief pharmacists from participating hospitals and is engaging with UK clinical pharmacists association to determine their priorities. At the time of making this application, a shortlist of questions that would be of value to them has been drawn up. An analytical team has been put in place specifically for the purpose of answering these questions and generating a report. The report delivered to each trust will contain aggregated, small number suppressed data across all trusts and will also contain trust-specific outputs which will only be shared with that trust. These findings will be sent to Chief Pharmacists and Research departments at participating hospital Trusts. IMS Health Ltd will also hold an annual research day for hospital trusts and other healthcare stakeholders to make people aware of the types of research that the HTI database has been used for and present the findings from the hospital trust specific studies. The 2017 HTI annual research day will be held in October 2017. Over time IMS Health Ltd expect the annual research day to be a forum for hospital trusts and arm’s length bodies to discuss the type of questions they want to answer so IMS Health Ltd can produce content that is relevant and directly benefits healthcare. 2. Regulatory Authorities and Arm’s Length Bodies (ALBs) involved in Health and Social Care. The specific outputs expected for regulatory authorities include drug safety studies, signal detection and evaluation of adverse drug reactions of hospital prescribed therapies. ALBs such as NICE, NHS Digital and NHS England will be able to access the pseudonymised, non-sensitive record level database for conducting health technology assessments, monitoring adherence to guidance and to inform policy decisions. 3. Medical researchers from academia. Access to pseudonymised, non-sensitive record level database or delivery of aggregated tables for generation of research publications in peer-reviewed journals and presentations in scientific conferences. Information to be included in health technology assessments and evidence for regulators. 4. Pharmaceutical companies. Provision of aggregated, small number suppressed tables to answer research questions in areas of: Advanced statistical analysis • epidemiology • natural history of disease • health economics and outcomes research • drug exposures • Drug safety monitoring • pharmacovigilance Where possible, the outputs from these studies will be published in peer-reviewed journals and presented at scientific conferences, included in Health Technology Assessments or delivered to regulators. Pharmaceutical companies are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day. Pharmaceutical companies will not have direct access to the pseudonymised record level data. ***Specific study outputs (since last application)*** Due to the time needed to load and validate the HTI database and gain ISEAC approval for studies, IMS Health Ltd have not completed any new studies since receiving the updated data in March 2017. One study was completed on previously held data whilst IMS Health Ltd’s DSA was being renewed.	Expected measurable benefits to Health and/or Social care included Target date Applications for access to HTI and HTI-CPRD-GOLD require researchers, internal to IMS Health Ltd. and external researchers operating under honorary contract, to articulate the public health benefits of their research. Trusts: HTI will help hospital trusts understand the use of and outcomes associated with hospital prescribed medicines. This will enable Trusts to undertake evidence based decisions on access to high cost drugs, support treatment policies, compare provision and outcomes with other Trusts and monitor patient outcomes. IMS Health Ltd will conduct studies on behalf of trusts and results from these studies will be shared as part of an annual research report and also at an annual research day which will be attended by participating trusts and other healthcare stakeholders. ALBs: HTI will be used to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. Academia: HTI will be used to undertake independent research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness of treatment. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. Pharma: HTI will be used to answer focused, scientific research questions with clear health and social care benefits. Such studies will include epidemiology, natural history of disease and health outcomes research. These studies will add to the body of research evidence used for drug development and the results of these studies can support optimal allocation of finite NHS resources. All studies will be published or a blinded version of results will be made available on IMS Global Bibliography meaning findings and information from the studies will be available to the healthcare and medical research community. In addition, pharmaceutical companies will conduct pharmacovigilance studies using HTI data. HTI data enables pharmaceutical companies to fulfil their regulatory requirements and keeps patients safe through identification and evaluations of adverse drug reaction. 1. Patient safety Adverse drug reactions (ADRs) create a burden for the NHS (Ref 3) and are an important cause of mortality amongst hospitalised patients. HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. There is an unmet need for this type of data as a study found that 50 % of newly licensed drugs are now solely prescribed in secondary care and therefore could not be monitored in widely used primary care databases (Ref 4). IMS Health Ltd has conducted two important safety studies in the HTI database and intends to carry out more. In one study, IMS Health Ltd were engaged by a drug’s marketing authorisation holder to conduct a three year post-authorisation safety study (PASS), a requirement for the marketing approval for this drug set out by the European Medicines Agency (EMA) on patients in England. The EMA requested the use of the drug be monitored using HTI. The indication associated with use of the drug was extracted from the database and the site of administration was determined as off label usage of this formulation in contra- indicated sites has been shown to cause significant harm. In the case of off-label usage then the drug manufacturer will need to update their risk management plan to prevent this from happening in the future and to prevent patient harm. This should be of significant interest to the NHS due to implication for preventable harm and the potential for litigation. A second study looked at the use of a marketed medicine which is known to have harmful effects in specific subpopulations of cancer patients. The regulator requested monitoring of exposure to the drug within these populations. This protects patients from receiving drugs which are contraindicated for them. The results of the study were submitted to the European Medicines Agency in support of a risk management plan (RMP) which helped to characterise the overall benefit risk profile the drug and ensures that it is used as safely as possible. Safety information which is included in the summary of product characteristics and on the drug’s package leaflet in based on the RMP so findings and guidance from this study are directly available to healthcare professionals and patients. Using HTI for safety studies allows quick analysis as the data already exists. If this database was not available, the two safety studies described would have taken months rather than days if conducted by other methods and only measured a smaller number of patients. Using a larger sample of patients ensures that studies are robust and enables the detection of rare events. 2. IMS Health Ltd have performed a number of studies on healthcare resource utilisation within patients prescribed highcost drugs Autoimmune conditions are complicated to manage and result in debilitating conditions for patients. Recent immunomodulating therapies such as anti TNF based drugs (all prescribed in secondary care) have been shown to provide considerable benefit to patients with a reduction in morbidity and improved quality of life. However, these drugs are expensive and the control and use within guidelines is important for NHS trusts with implications for those involved with commissioning fully funded pathways. IMS Health Ltd has conducted a series of epidemiological studies in this therapy area to determine the dosing patterns, the indications for which the drugs are prescribed and the patient populations within which they are used. It has been shown that high cost drugs (anti-TNFs and biologics) are used more frequently in routine clinical practice than anticipated. This creates an additional cost burden to the NHS then planned for. Using HTI data IMS Health Ltd showed that inflammatory bowel disease patients treated with high cost drugs showed differences in the rates of hospitalisation and surgical interventions between different agents (Ref 1). This information can be used to identify patient groups that would benefit most from these high-cost drugs and allow resources to be allocated accordingly. This piece of work has been disseminated at one of the leading European conferences, the United European Gastroenterology Week (UEG). Attendees at the UEG include leading specialists across gastroenterology making it a key opportunity for knowledge sharing across the gastroenterology community. It has also been published via an open access journal PLOS One this means that NHS staff are able to access this for free via a standard literature search for evidence meaning there is no paywall standing in the way of health professionals accessing this material. 3. Probability of hospitalisation Intravenous iron therapy is not considered as first line treatment of iron deficiency anaemia in the majority of patients. IMS Health Ltd conducted an epidemiological study in collaboration with a pharmaceutical company to show that the 30- day readmission rates among those patients treated with IV were significantly lower than those treated with oral therapies (Ref 2). Readmission to hospital is distressing for patients but is also an inefficient use of NHS resources. 30 day readmission rate is a key quality metric that is used to evaluate NHS Trusts. This study was presented at the Digestive Disorders Federation's annual scientific meeting and its inclusion was decided by a panel of gastrointestinal experts. 4. Adherence to medication It is well known that nonadherence to medications can result in worsening clinical outcomes, including rehospitalisation, exacerbation of chronic medical conditions, increased healthcare costs and death [ref 5]. IMS Health Ltd and the CPRD conducted a feasibility study to investigate the % of patients who continued treatment of anticoagulant therapy in primary care within 90 days of discharge from hospital. Anticoagulants are effective for prevention of strokes and heart attacks but adherence to medication is crucial for maximising treatment benefits. The HTI-CPRD data showed that further anticoagulation therapy was continued in primary care in over half of hospitalisations discharged with these medications. This study was presented as a poster at the International Conference of Pharmacoepidemiology (ICPE) in 2016 [ref 6]. Ref 1. Dose Escalation and Healthcare Resource Use among Ulcerative Colitis Patients Treated with Adalimumab in English Hospitals: An Analysis of Real-World Data Christopher M. Black1, Eric Yu2, Eilish McCann3, Sumesh Kachroo1* 1 Merck &Co, Inc., Kenilworth, United States of America, 2 IMS Health Ltd, London, United Kingdom, 3 Merck Sharp & Dohme Ltd, Hoddesdon, United Kingdom http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149692 Ref 2. ASSOCIATION OF ORAL AND INTRAVENOUS IRON WITH THE PROBABILITY OF HOSPITALIZATION IN ENGLAND S.Keshav 1, C. Chapman 2, S. Tomkins 3,*, L. Mills 4, B. Jackson 41 Translational Gastroenterology Unit, John Radcliffe Hospital and University of Oxford, Oxford, 2 West Middlesex University Hospital, Isleworth, 3Real World Evidence, IMS Health, London, 4Vifor Pharma, Bagshot, United Kingdom http://gut.bmj.com/content/64/Suppl_1/A18.2 Ref 3. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients Munir Pirmohamed, professor of clinical pharmacology,1 Sally James, research pharmacist,3 Shaun Meakin, research nurse,2 Chris Green, senior pharmacist,2 Andrew K Scott, consultant in care of the elderly,3 Thomas J Walley, professor of clinical pharmacology,1 Keith Farrar, chief pharmacist,3 B Kevin Park, professor of pharmacology,1 and Alasdair M Breckenridge, professor of clinical pharmacology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC443443/ Ref 4. S. Cederholm, G. Hill, A. Asiimwe, A. Bate, F. Bhayat, G. Persson Brobert, T. Bergvall, D. Ansell, K. Star, and G. N. Norén. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.Drug Saf. 2015 Jan;38(1):87-100. doi: 10.1007/s40264-014-0251-y.PMID:25539877) http://www.who-umc.org/graphics/29625.pdf Ref 5. Robin Dimatteo M, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes A meta-analysis. Medical Care. 2002;40(9):794–811. [PubMed] Ref 6. Gallagher AM, Rockenschaub P, Tham R, Dattani H, Collier A, de Vries F, Williamsm T, Evaluating the utility of the CPRD GOLD-HTI linkage: anticoagulant prescribing at the GP practice compared to hospital dispensed medication at discharge date, ICPE 2016
IMS HEALTH LTD	IMS HEALTH LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	IMS Health Ltd is an information and technology company serving the health care industry. IMS Health Ltd produces a longitudinal research database, Hospital Treatment Insights (HTI) and in collaboration with the CPRD produces HTI-CPRD-GOLD. This database contains unique information on diagnosis, treatment and drug usage across primary and secondary care in England and HTI is currently the only routinely collected population based database available for monitoring the safety of medicines used in the secondary care setting. NHS Digital acts as a Trusted Third Party and provides linkage services for both HTI and HTI-CPRD-GOLD datasets. The HTI data links record level pseudonymised, non-sensitive HES data to IMS Health Ltd’s unique database of hospital prescribing data. The HTI-CPRD-GOLD data is a subset of the HTI data and links record level HTI data to CPRD-GOLD data. Currently there are 6.4 million patients in HTI data and 650,000 patients in HTI-CPRD-GOLD. The HTI and HTI-CPRD-GOLD data are treated as separate databases for study purposes so a researcher must specify which data they are requesting access to. Researchers accessing these datasets will be substantive employees of IMS Health Ltd or external researchers who have signed an NHS Digital approved honorary contract with IMS Health Ltd. 1. Data will be used to undertake a programme of research studies, in two main areas, aimed at promoting public health which will include: Advanced statistical analysis • Epidemiology • natural history of disease • health economics and outcomes research • drug exposures Drug safety monitoring • monitoring of adverse events for newly licenced drugs prescribed in secondary care • pharmacovigilance In all cases, purposes are restricted to those for the provision of health care or adult social care, or the promotion of health. Access to the data will be as follows: 1. Regulatory Authorities. Regulators such as the Medicines and Healthcare Products Regulatory Agency (MHRA) and the European Medicines Agency (EMA) will investigate potential adverse drug reactions (ADR) in marketed products. When new ADRs are discovered regulators can recommend actions to limit harm to patients exposed to the products. Responsiveness is key as products are already marketed meaning that further events could occur immediately. Therefore it is crucial to have data, like HTI and HTI-CPRD-GOLD, readily available that will allow direct investigation of ADR or evaluation of the likely public health impact should the potential ADR be established as a real effect. Examples of current studies: IMS Health Ltd is in discussions with researchers who have a special interest in drug safety with a view to conducting a proof of concept drug safety study in HTI. Regulators, such as the MHRA and EMA, have also expressed an interest in using HTI data in their drug safety activities. They currently have access to data sources that collect information on drug exposure and clinical events in primary care. However, these regulators are also responsible for pharmacovigilance of products that are only used in a hospital setting. Currently they have no directly accessible data on the use of these drugs and clinical events in patients exposed to the products except those supplied via spontaneous reporting systems. The latter are frequently the source of signals regarding potential ADRs and hence have limited use in further investigation of the signal. For this reason regulators are in need of information on drug use in a hospital setting. Thus access to HTI data has an important role in improving the use of medicines used in hospitals and could have a significant impact on public health. 2. Arm’s Length Bodies (ALBs) involved in Health and Social Care. ALBs, such as NICE, can use HTI to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. HTI will allow the study of key performance measures used by the NHS, in association with pharmaceutical treatments in the following areas: • Uptake and utilisation of new and existing therapies • Medical vs surgical treatment rates associated with specific pharmaceutical treatments • Readmission rates associated with specific pharmaceutical treatments • Rates of elective vs non elective admissions in patients following different treatment regimens IMS Health Ltd is currently working with an academic researcher who sits on NICE’s appraisal committee to investigate equity of access to high cost drugs. This work would help to identify trends and variation in secondary care prescribing of high cost drugs across the country and by socioeconomic status and could be used to support national policy. 3. Medical researchers from academia and other types of organisation such as patient groups, charitable trusts and pharmaceutical companies. HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. The topic of sustainability will be a focus area for researchers, particularly as 94% of new molecular entities will be for specialist care delivered within the hospital setting. Current academic relationships: London School of Hygiene and Tropical Medicine (Faculty of Epidemiology and Population Health) ʹworking with the epidemiology group to study the cardiovascular outcomes of varying cancer treatments in survivors of breast cancer. University College London (Dept. of Public Health) ʹa validation study to determine the strengths and limitations of the data for antibiotic research also a second piece of work on babies diagnosed with Respiratory syncytial virus treated with Pavalizumab. Clinical Practice Research Datalink – A validation study looking at whether patients discharged from hospital continue their medication in primary care. A feasibility study looking at NOAC prescribing on discharge has already been conducted and a full study will be undertaken using the updated data. . There are controls in place to ensure secure access to the data: 1. Researcher access - the HTI and HTI-CPRD-GOLD databases contain pseudonymised data. All researchers accessing the data need to be substantive employees of IMS Health Ltd or must have an honorary contract with IMS Health Ltd in place. All researchers accessing these data undertake training and sign additional confidentiality agreements with IMS Health Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. IMS Health Ltd does not permit any access to pseudonymised patient record-level data from outside of the UK. 2. Strong internal governance process - researchers only access the HTI and HTI-CPRD-GOLD data for single-study research projects that have received approval from IMS’ Independent Scientific Ethical Advisory Committee (ISEAC) for HTI studies and the CPRD͛s Independent Scientific Advisory Committee (ISAC) for HTI-CPRD-GOLD studies. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval. 3. Advanced study planning - further safeguards include the standard IMS Health Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol and statistical analysis plan (SAP). IMS Health Ltd maintains an access control register and all usage of the database against an ISEAC or ISAC approved protocol is logged and auditable. Where appropriate, researchers accessing HTI or HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included on the publically available IMS global bibliography. IMS Health Ltd will share these findings with participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd will not make the outputs of safety studies publically available to avoid generating undue public concern before guidance is issued by regulatory agencies. Data minimisation: IMS Health Ltd understand the importance of data minimisation and have taken steps to reduce the number of HES records requested. IMS is requesting access to the following data: • HES records, either in an HTI trust or a non-HTI Trust, that can be linked to a pharmacy record • All other HES records from HTI Trusts that can't be linked to a pharmacy record. These records will only be used for data validation purposes to compare the % of linkage across different trusts and therapy areas. These records will only be accessible to researchers for data validation purposes and will be used to indicate whether the data is high quality enough for medical research studies. Justification for historical data - IMS Health Ltd uses historical HES data (from 2005 where available) in HTI studies in order to identify diagnosis prior to patients receiving a drug, determine any co-morbid conditions and identify the date when a patient first had a secondary care visit for a particular diagnosis (index date). HTI has been used to conduct studies on drug treatment for chronic conditions including psoriasis, rheumatoid arthritis, multiple sclerosis and ulcerative colitis. Patients with chronic diseases have these conditions for life so it is important to have the maximum number of years of back data in order to conduct studies rigorously. When researchers conduct studies using HTI they need to establish the index date of a patient at the beginning of treatment or diagnosis in order to determine progress and treatment efficacy over a follow up period. They also need to understand if patients have a history of serious comorbid conditions e.g. if a patient was hospitalised 10 years ago for a stroke then this needs to be taken into account. By answering these questions researchers are able to build cohorts for studies with the right type of characteristics. If historical HES data was not provided then researchers could miss important events which would then not be adjusted for in study results. In addition the historical data will be used to detect rare, delayed adverse events. Researchers from regulatory agencies need access to historical HES data in the HTI database in order to monitor drug safety, particularly of rare and delayed adverse events which may take many years to develop. Further scientific need for the historical data – • Historic data is required to support Advanced Statistical Analysis projects and safety studies, as historical data allows robust analysis of trends over time. • Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” • Historical data is needed for patients with chronic conditions to understand disease progression and can be used to investigate how the usage of different treatments impacts the typical time of disease progression • Historical data is needed to understand previous and co-morbid conditions in order to adjust for these in the research study outcome. For example a trust could be incorrectly identified as having poor outcomes or performance when they are in fact treating sicker or higher risk patients e.g. patients with previous cardiovascular and stroke events. This information in also needed to ensure the research outcome is correctly interpreted so that the medical professionals are able to provide the most appropriate care for patients e.g. a medication may be considered to have higher risk profile in a certain patient population • Historical data provides extended longitudinal coverage to allow researchers to look at delayed adverse events or outcomes which have a long latency period from the time of exposure to manifestation. Further justification for the historical data requirement - 1. HTI is unique in that it enables the study of hospital only medicines in a real world setting. This is very important because the patients receiving these medicines don’t always reflect those that the medicines are tested in. Many HTI studies are observational studies of the way in which drugs are used i.e. characterising the patients that receive them and then following those patients to see whether or not they benefit from the drug(s) in question. These studies are conducted over a specified study period i.e. a time frame over which the observation takes place. Setting the time period requires a starting point or ‘index’ date. This is usually the date at which a diagnosis has been made for which the drug being studied is a treatment. For chronic conditions diagnoses are made early in the patients’ history and the historic HES data enables an accurate index date to be set. Without the historic data, important information around the diagnosis of the disease would be missed. 2. HTI has been used to study the use of biologics in the treatment of autoimmune diseases such as ulcerative colitis and rheumatoid arthritis. These are expensive hospital only drugs and are not the first line of therapy for these diseases. The studies we have conducted have looked at healthcare resource use prior to the administration of these drugs i.e. the treatment pathway leading up to the administration of these drugs. Without the historic HES data, these studies would not be possible. 3. One very important use of HTI is in drug safety studies. These studies can sometimes involve identifying potential ‘signals’ or side effects of a drug. For these studies it is vital to access as much of a patient’s past medical history as possible. For example, some new drugs may have the potential to cause cardiovascular side effects, in order to evaluate these it would be important to know if a patients has suffered from cardiovascular problems in the past and to include as much detail as possible around those events into the analysis. 4. Some studies look at the difference in outcomes associated with different treatment options for a particular disease i.e. what are the characteristics of patients that respond better to one treatment over another. These studies are important because they can help doctors decide which treatment option would be more suitable for a patient. In order to conduct these studies with rigor and as much accuracy as possible, it is important to take into account every aspect the patient’s health state from their history because the past medical history affects the decisions that doctors take for current and future drug therapy. 5. Large databases like HTI are often used to conduct observational epidemiological studies. The starting point of these studies requires building a cohort of study patients based on strict inclusion and exclusion criteria. Without access to the past medical history, it is difficult to ensure that the patients included in the study are the ‘right’ patients and the study results could be biased. 6. For the study of chronic and rare diseases and delayed side effects of medicines, a long past medical history is important because the lead up to diagnosis of such conditions can be complex and lengthy. 7. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis” 8. Past medical history often includes risk factors, co-morbidities and confounders that are important to adjust for when analysing data in order to ensure that conclusions drawn from the research are generalizable and robust.	Processing Activities Data flow Each month, participating trusts provide three files to NHS Digital. The 'TRUSTED' file contains patient identifiable hospital pharmacy issues data, which is used for the subsequent linkage to HES. The 'ISSUES' file is a non-identifiable version of the TRUSTED file which NHS Digital provides onward to IMS Health Ltd, and also checks against the TRUSTED file to ensure the payload data in the two files are consistent. A third data definition file ͚’DEFS͛’ is also provided to NHS Digital which is forwarded to IMS Health Ltd. The definitions file contains details of the drug such as name and the ward that issued it, does not contain identifiable data. The ISSUES and DEFS files (both of which contain no identifiable data) are received by IMS Health Ltd for their hospital pharmacy audit work, which is outside the scope of this agreement. Hospital prescribing and HES data are linked by NHS Digital and data are pseudonymised before being passed on to IMS Health Ltd on a quarterly basis. Once received, these data are downloaded via SFTP to a secure server within an ISO27001 accredited environment. Security measures include: • Access authentication • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions Remote Access to IMS ISO27001 compliant environment: Researchers access the IS027001 environment remotely via a secure portal. Researchers are able to query the data within this environment and create patient cohorts for further study. Data cannot be copied from the secure environment and usage of the secure environment is auditable. Researchers can export aggregated, small number suppressed data from the secure environment and a record of all exports is kept for monitoring and audit purposes. External Researcher access: Access to the data is only permitted for substantive IMS Health Ltd employees and researchers working under honorary contract to IMS Health Ltd. The honorary contract in use has previously been approved by DAAG and obligates the researcher to adhere to the terms and conditions of IMS Health Ltd.’s DSA. Honorary contractors are subject to the same access controls as substantive IMS Health Ltd employees. They are provided with a username and password and access the data through the secure portal. If an honorary contractor is accessing data from any location apart from the IMS Health Ltd office they will be required to provide details of the processing location in their honorary contract. IMS Health Ltd validates that the data processing location listed has appropriate security measures in place such as ISO27001 or IG Toolkit before access to the data is granted. Access to the data is not permitted from outside the UK. IMS Health Ltd reserves the right to undertake an audit of the honorary contractor at any time to ensure that appropriate security measures are in place and that all terms of the agreement are being abided by (such as agreed processing location). IMS Health Limited are not permitted to approve or otherwise authorise the use of the data supplied by NHS Digital (or predecessor organisations) for any additional purposes other than those described in the Purpose/Methods/Outputs section. No new projects or purposes are permitted under this agreement. Data cannot be used for solely commercial purposes as per the requirements of the Care Act 2014 Clause for preventing commercial use IMS Health Ltd will to include the wording below in contracts with external organisations. “NHS Digital provides IMS Health Ltd with Hospital Episode Statistics in accordance with the amended section 261 of the Health and Social Care Act 2012 i.e. “if it considers that disseminating the information would be for the purposes of— a) the provision of health care or adult social care, or b) the promotion of health.” Therefore, the CLIENT is not permitted to use the HES analytical services provided by IMS Health Ltd to do the following: • analysis for insurance companies • analysis to identify or communicate with directly with patients (or groups thereof) for purposes other than their direct care • analysis to identify or communicate with clinicians for solely sales or marketing • analysis to quantify the impact of marketing campaigns • analysis to inform salesforce structure or to understand salesforce performance” Data sharing with third party organisations under sub-licence is not permitted under this agreement. Record level data or aggregate data containing small numbers will not be shared within the IMS group of organisations.	5c. Specific Outputs expected, Including Target Date All HTI and HTI-CPRD-GOLD studies result in a scientific report structured along the lines of a scientific paper (e.g. Summary, Background, Methods, Results and Discussion). Interim tables of results (aggregated data with small number suppression in line with the HES analysis guide) may be circulated as interim results for discussion and appended to the study report. Further outputs include research publications in peer-reviewed journals and presentations at scientific conferences in addition to research included in Health Technology Assessments and Regulatory evidence. Researchers accessing HTI and HTI-CPRD-GOLD data are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be included in the publically available IMS Global bibliography. IMS Health Ltd will present these results to participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd received updated HTI data on 21st Feb 2017 and received a further update on 29th March 2017. Having received the data, IMS Health Ltd have focussed on optimising the database design and database validation. This work will be completed by the end of April 2017 and the database will be available for researchers to access in May 2017. As all previously held data was deleted on receipt of the updated data no research is taking place during this period. The following outputs are expected for each of the research groups IMS Health Ltd engages with: 1. Participating NHS Trusts. IMS Health Ltd.’s research team is working with NHS Trusts to assist in the production of information that will impact on improving patient care. IMS Health Ltd have conducted interviews among chief pharmacists from participating hospitals and is engaging with UK clinical pharmacists association to determine their priorities. At the time of making this application, a shortlist of questions that would be of value to them has been drawn up. An analytical team has been put in place specifically for the purpose of answering these questions and generating a report. The report delivered to each trust will contain aggregated, small number suppressed data across all trusts and will also contain trust-specific outputs which will only be shared with that trust. These findings will be sent to Chief Pharmacists and Research departments at participating hospital Trusts. IMS Health Ltd will also hold an annual research day for hospital trusts and other healthcare stakeholders to make people aware of the types of research that the HTI database has been used for and present the findings from the hospital trust specific studies. The 2017 HTI annual research day will be held in October 2017. Over time IMS Health Ltd expect the annual research day to be a forum for hospital trusts and arm’s length bodies to discuss the type of questions they want to answer so IMS Health Ltd can produce content that is relevant and directly benefits healthcare. 2. Regulatory Authorities and Arm’s Length Bodies (ALBs) involved in Health and Social Care. The specific outputs expected for regulatory authorities include drug safety studies, signal detection and evaluation of adverse drug reactions of hospital prescribed therapies. ALBs such as NICE, NHS Digital and NHS England will be able to access the pseudonymised, non-sensitive record level database for conducting health technology assessments, monitoring adherence to guidance and to inform policy decisions. 3. Medical researchers from academia. Access to pseudonymised, non-sensitive record level database or delivery of aggregated tables for generation of research publications in peer-reviewed journals and presentations in scientific conferences. Information to be included in health technology assessments and evidence for regulators. 4. Pharmaceutical companies. Provision of aggregated, small number suppressed tables to answer research questions in areas of: Advanced statistical analysis • epidemiology • natural history of disease • health economics and outcomes research • drug exposures • Drug safety monitoring • pharmacovigilance Where possible, the outputs from these studies will be published in peer-reviewed journals and presented at scientific conferences, included in Health Technology Assessments or delivered to regulators. Pharmaceutical companies are required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day. Pharmaceutical companies will not have direct access to the pseudonymised record level data. ***Specific study outputs (since last application)*** Due to the time needed to load and validate the HTI database and gain ISEAC approval for studies, IMS Health Ltd have not completed any new studies since receiving the updated data in March 2017. One study was completed on previously held data whilst IMS Health Ltd’s DSA was being renewed.	Expected measurable benefits to Health and/or Social care included Target date Applications for access to HTI and HTI-CPRD-GOLD require researchers, internal to IMS Health Ltd. and external researchers operating under honorary contract, to articulate the public health benefits of their research. Trusts: HTI will help hospital trusts understand the use of and outcomes associated with hospital prescribed medicines. This will enable Trusts to undertake evidence based decisions on access to high cost drugs, support treatment policies, compare provision and outcomes with other Trusts and monitor patient outcomes. IMS Health Ltd will conduct studies on behalf of trusts and results from these studies will be shared as part of an annual research report and also at an annual research day which will be attended by participating trusts and other healthcare stakeholders. ALBs: HTI will be used to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality. Academia: HTI will be used to undertake independent research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness of treatment. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. Pharma: HTI will be used to answer focused, scientific research questions with clear health and social care benefits. Such studies will include epidemiology, natural history of disease and health outcomes research. These studies will add to the body of research evidence used for drug development and the results of these studies can support optimal allocation of finite NHS resources. All studies will be published or a blinded version of results will be made available on IMS Global Bibliography meaning findings and information from the studies will be available to the healthcare and medical research community. In addition, pharmaceutical companies will conduct pharmacovigilance studies using HTI data. HTI data enables pharmaceutical companies to fulfil their regulatory requirements and keeps patients safe through identification and evaluations of adverse drug reaction. 1. Patient safety Adverse drug reactions (ADRs) create a burden for the NHS (Ref 3) and are an important cause of mortality amongst hospitalised patients. HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. There is an unmet need for this type of data as a study found that 50 % of newly licensed drugs are now solely prescribed in secondary care and therefore could not be monitored in widely used primary care databases (Ref 4). IMS Health Ltd has conducted two important safety studies in the HTI database and intends to carry out more. In one study, IMS Health Ltd were engaged by a drug’s marketing authorisation holder to conduct a three year post-authorisation safety study (PASS), a requirement for the marketing approval for this drug set out by the European Medicines Agency (EMA) on patients in England. The EMA requested the use of the drug be monitored using HTI. The indication associated with use of the drug was extracted from the database and the site of administration was determined as off label usage of this formulation in contra- indicated sites has been shown to cause significant harm. In the case of off-label usage then the drug manufacturer will need to update their risk management plan to prevent this from happening in the future and to prevent patient harm. This should be of significant interest to the NHS due to implication for preventable harm and the potential for litigation. A second study looked at the use of a marketed medicine which is known to have harmful effects in specific subpopulations of cancer patients. The regulator requested monitoring of exposure to the drug within these populations. This protects patients from receiving drugs which are contraindicated for them. The results of the study were submitted to the European Medicines Agency in support of a risk management plan (RMP) which helped to characterise the overall benefit risk profile the drug and ensures that it is used as safely as possible. Safety information which is included in the summary of product characteristics and on the drug’s package leaflet in based on the RMP so findings and guidance from this study are directly available to healthcare professionals and patients. Using HTI for safety studies allows quick analysis as the data already exists. If this database was not available, the two safety studies described would have taken months rather than days if conducted by other methods and only measured a smaller number of patients. Using a larger sample of patients ensures that studies are robust and enables the detection of rare events. 2. IMS Health Ltd have performed a number of studies on healthcare resource utilisation within patients prescribed highcost drugs Autoimmune conditions are complicated to manage and result in debilitating conditions for patients. Recent immunomodulating therapies such as anti TNF based drugs (all prescribed in secondary care) have been shown to provide considerable benefit to patients with a reduction in morbidity and improved quality of life. However, these drugs are expensive and the control and use within guidelines is important for NHS trusts with implications for those involved with commissioning fully funded pathways. IMS Health Ltd has conducted a series of epidemiological studies in this therapy area to determine the dosing patterns, the indications for which the drugs are prescribed and the patient populations within which they are used. It has been shown that high cost drugs (anti-TNFs and biologics) are used more frequently in routine clinical practice than anticipated. This creates an additional cost burden to the NHS then planned for. Using HTI data IMS Health Ltd showed that inflammatory bowel disease patients treated with high cost drugs showed differences in the rates of hospitalisation and surgical interventions between different agents (Ref 1). This information can be used to identify patient groups that would benefit most from these high-cost drugs and allow resources to be allocated accordingly. This piece of work has been disseminated at one of the leading European conferences, the United European Gastroenterology Week (UEG). Attendees at the UEG include leading specialists across gastroenterology making it a key opportunity for knowledge sharing across the gastroenterology community. It has also been published via an open access journal PLOS One this means that NHS staff are able to access this for free via a standard literature search for evidence meaning there is no paywall standing in the way of health professionals accessing this material. 3. Probability of hospitalisation Intravenous iron therapy is not considered as first line treatment of iron deficiency anaemia in the majority of patients. IMS Health Ltd conducted an epidemiological study in collaboration with a pharmaceutical company to show that the 30- day readmission rates among those patients treated with IV were significantly lower than those treated with oral therapies (Ref 2). Readmission to hospital is distressing for patients but is also an inefficient use of NHS resources. 30 day readmission rate is a key quality metric that is used to evaluate NHS Trusts. This study was presented at the Digestive Disorders Federation's annual scientific meeting and its inclusion was decided by a panel of gastrointestinal experts. 4. Adherence to medication It is well known that nonadherence to medications can result in worsening clinical outcomes, including rehospitalisation, exacerbation of chronic medical conditions, increased healthcare costs and death [ref 5]. IMS Health Ltd and the CPRD conducted a feasibility study to investigate the % of patients who continued treatment of anticoagulant therapy in primary care within 90 days of discharge from hospital. Anticoagulants are effective for prevention of strokes and heart attacks but adherence to medication is crucial for maximising treatment benefits. The HTI-CPRD data showed that further anticoagulation therapy was continued in primary care in over half of hospitalisations discharged with these medications. This study was presented as a poster at the International Conference of Pharmacoepidemiology (ICPE) in 2016 [ref 6]. Ref 1. Dose Escalation and Healthcare Resource Use among Ulcerative Colitis Patients Treated with Adalimumab in English Hospitals: An Analysis of Real-World Data Christopher M. Black1, Eric Yu2, Eilish McCann3, Sumesh Kachroo1* 1 Merck &Co, Inc., Kenilworth, United States of America, 2 IMS Health Ltd, London, United Kingdom, 3 Merck Sharp & Dohme Ltd, Hoddesdon, United Kingdom http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149692 Ref 2. ASSOCIATION OF ORAL AND INTRAVENOUS IRON WITH THE PROBABILITY OF HOSPITALIZATION IN ENGLAND S.Keshav 1, C. Chapman 2, S. Tomkins 3,*, L. Mills 4, B. Jackson 41 Translational Gastroenterology Unit, John Radcliffe Hospital and University of Oxford, Oxford, 2 West Middlesex University Hospital, Isleworth, 3Real World Evidence, IMS Health, London, 4Vifor Pharma, Bagshot, United Kingdom http://gut.bmj.com/content/64/Suppl_1/A18.2 Ref 3. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients Munir Pirmohamed, professor of clinical pharmacology,1 Sally James, research pharmacist,3 Shaun Meakin, research nurse,2 Chris Green, senior pharmacist,2 Andrew K Scott, consultant in care of the elderly,3 Thomas J Walley, professor of clinical pharmacology,1 Keith Farrar, chief pharmacist,3 B Kevin Park, professor of pharmacology,1 and Alasdair M Breckenridge, professor of clinical pharmacology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC443443/ Ref 4. S. Cederholm, G. Hill, A. Asiimwe, A. Bate, F. Bhayat, G. Persson Brobert, T. Bergvall, D. Ansell, K. Star, and G. N. Norén. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.Drug Saf. 2015 Jan;38(1):87-100. doi: 10.1007/s40264-014-0251-y.PMID:25539877) http://www.who-umc.org/graphics/29625.pdf Ref 5. Robin Dimatteo M, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes A meta-analysis. Medical Care. 2002;40(9):794–811. [PubMed] Ref 6. Gallagher AM, Rockenschaub P, Tham R, Dattani H, Collier A, de Vries F, Williamsm T, Evaluating the utility of the CPRD GOLD-HTI linkage: anticoagulant prescribing at the GP practice compared to hospital dispensed medication at discharge date, ICPE 2016
INSTITUTE OF CANCER RESEARCH	INSTITUTE OF CANCER RESEARCH	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The study aims to find genes which predispose to prostate cancer, and to determine whether variation in risk genes influence the prognosis of individuals with prostate cancer. 31 regions of the genome have now been associated with prostate cancer (the majority found by our research group), and we can now begin to analyse what effects these “risk regions” have on prostate cancer aggressiveness and outcome. Our aims are to find new genetic markers to identify who might be at risk of developing prostate cancer in the future; to be able to more accurately target appropriate treatments to those patients who are most likely to have aggressive disease and to develop new drug treatments for prostate cancer. This has the potential to be able to target screening to those who would benefit from this manoeuvre and the potential to improve survival. (see point 10)	Standard time-to-event analysis will be used to test for association between genotype and prognosis. Time at risk will begin on the date of diagnosis, with time under observation beginning on date of blood sample receipt. Follow-up will be censored on the date of death from any cause, or, if death did not occur, on the date of the end of the study. We already have genotype data, date of diagnosis and date of blood receipt for all our cases. We therefore request death certificate data to enable us to obtain date of death and cause of death for those men in UKGPCS who have died. (see point 12)	The study aims to find genes which predispose to prostate cancer, and to determine whether variation in risk genes influence the prognosis of individuals with prostate cancer	The UKGPCS has been running since 1992, and gained MREC approval in 2003. The study is UK wide and currently 162 hospitals recruit prostate cancer patients with young onset or familial prostate cancer into the study. The Royal Marsden Hospital has ethical approval to recruit prostate cancer patients of any age to the study. Currently there are 10,491 patients consented to the study, and the target is 21,000 by the end of 2012. Each consented patient gives a blood sample, and a family history questionnaire, and we obtain clinical data from their consultant. Not all patients have given consent to re contact them and we do not have current addresses for many of the participants, some of whom were recruited more than 10 years ago.
INSTITUTE OF CANCER RESEARCH	INSTITUTE OF CANCER RESEARCH	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The study aims to find genes which predispose to prostate cancer, and to determine whether variation in risk genes influence the prognosis of individuals with prostate cancer. 31 regions of the genome have now been associated with prostate cancer (the majority found by our research group), and we can now begin to analyse what effects these “risk regions” have on prostate cancer aggressiveness and outcome. Our aims are to find new genetic markers to identify who might be at risk of developing prostate cancer in the future; to be able to more accurately target appropriate treatments to those patients who are most likely to have aggressive disease and to develop new drug treatments for prostate cancer. This has the potential to be able to target screening to those who would benefit from this manoeuvre and the potential to improve survival. (see point 10)	Standard time-to-event analysis will be used to test for association between genotype and prognosis. Time at risk will begin on the date of diagnosis, with time under observation beginning on date of blood sample receipt. Follow-up will be censored on the date of death from any cause, or, if death did not occur, on the date of the end of the study. We already have genotype data, date of diagnosis and date of blood receipt for all our cases. We therefore request death certificate data to enable us to obtain date of death and cause of death for those men in UKGPCS who have died. (see point 12)	The study aims to find genes which predispose to prostate cancer, and to determine whether variation in risk genes influence the prognosis of individuals with prostate cancer	The UKGPCS has been running since 1992, and gained MREC approval in 2003. The study is UK wide and currently 162 hospitals recruit prostate cancer patients with young onset or familial prostate cancer into the study. The Royal Marsden Hospital has ethical approval to recruit prostate cancer patients of any age to the study. Currently there are 10,491 patients consented to the study, and the target is 21,000 by the end of 2012. Each consented patient gives a blood sample, and a family history questionnaire, and we obtain clinical data from their consultant. Not all patients have given consent to re contact them and we do not have current addresses for many of the participants, some of whom were recruited more than 10 years ago.
INSTITUTE OF CANCER RESEARCH	INSTITUTE OF CANCER RESEARCH	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The data supplied by the NHS IC to Institute of Cancer Research will be used only for the approved Medical Research Project - Breakthrough Generations Study.
INSTITUTE OF CANCER RESEARCH	INSTITUTE OF CANCER RESEARCH	MRIS - Cause of Death Report	Identifiable	Sensitive	Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007	Ongoing	Y	The data supplied will be used only for the approved Medical Research Project - MR400 - Cohort Study of People with Insulin Treated Diabetes
INSTITUTE OF CANCER RESEARCH	INSTITUTE OF CANCER RESEARCH	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The data supplied by the NHS IC to Institute of Cancer Research will be used only for the approved Medical Research Project - Breakthrough Generations Study.
INSTITUTE OF OCCUPATIONAL MEDICINE (IOM)	INSTITUTE OF OCCUPATIONAL MEDICINE (IOM)	MRIS - Members and Postings Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Inorganic lead compounds are classified, by the International Agency for Research on Cancer (IARC) which is an agency of the World Health Organisation, as 'probably carcinogenic to humans'. This is based on ‘sufficient evidence of carcinogenicity in experimental animals’ and 'limited evidence of carcinogenicity in humans’. Much of the epidemiological evidence for carcinogenic effects in humans comes from studies of lead workers. However, previous studies have often involved relatively small numbers of workers and suffered from methodological constraints such as limited information on co-exposures to other risk factors or poorly characterised exposure assessments. Given the length of follow-up, the large size of the cohort and the relatively high exposures that they experienced (as documented by blood lead levels), the proposed study would make a significant contribution to the evidence on the carcinogenicity of lead exposure. This study will fill an important knowledge gap identified as a research priority by IARC.	Only substantive employees of Institute of Occupational Medicine (IOM) will have access to the disseminated data and only for the purposes described in this document. Standard Office for National Statistics term s and conditions apply to the mortality data already being held by this study. This cohort is already flagged with NHS Digital. IOM would like to obtain cancer register data linked to the Lead study cohort. Identifiable data is being requested and includes; NHS Number, name, DOB, sex as well as cancer site, type and morphology data and member number. Identifiable data has been requested to ensure the quality of the linkage and the subsequent analysis. The cancer data will be analysed, along with the occupational data (blood lead measurement, factory and process codes) to compare exposures, types and rates and will be compared will national statistics. De-identified data in the format of IARC member number, cancer site, type and morphology linked to occupational data will be supplied to IARC via a secure transfer method. No identifiable data will be shared with IARC, including date of the cancer. IARC do not hold any identifiers for the cohort, so cannot re-identify the cohort on receipt on the data from IOM. IOM are the data controller for the data supplied to IARC. The de-identified data being shared with IARC will be pooled with other international study outputs to increase the statistical information available to inform IARCs classification and occupational policy makers. The Institute of Occupational Medicine are prohibited from providing identifiable data to International Agency for Research on Cancer. The Institute of Occupational Medicine, as Data Controller, are responsible for the disseminated data, including the data shared with the International Agency for Research on Cancer. Therefore, IOM will be considered in breach of this agreement should IARC break any of the conditions of the Data Sharing Agreement agreed between IOM and IARC. If IARC does not respond in a timely manner to a request made for necessary evidence to ensure that the terms of their data sharing agreement with IOM are being abided by, then IOM is responsible for informing NHS Digital of this. IOM are responsible for ensuring that data destruction is completed at IARC when required.	Once the processing for this study has been completed, there will exist an international data sets in relation to cancer incidence risks within the lead manufacturing industry. As a consequence of this study the International Agency for Research on Cancer will revisit their classification of work in the lead industry. The institute of Occupational Medicine have strong links with the Health and Safety Executive and will also report their findings directly to the HSE. There will also be a peer-reviewed scientific publication of an analysis of the UK, Finnish and US cancer registration data. This is expected to be published one of the following journals; • Occupational and Environmental Medicine • Journal of Occupational and Environmental Medicine • American Journal of Industrial Medicine It is expected to be submitted for publication mid 2017. Example of a previous publication can be found on BMJ http://oem.bmj.com/content/72/9/625 The relevant Trade Unions will also be informed of the outputs so they can make their members aware.	At present there is no clear evidence that occupational exposures in the lead manufacturing industry increases workers’ cancer risks. The International Agency for Research on Cancer (IARC) has occupational exposure to inorganic lead as ‘probably carcinogenic to humans’ based on limited evidence of carcinogenicity in humans. Therefore it is a priority to clarify whether exposure increases risk of cancer. This study will identify whether there are any health risks from having worked in the lead manufacturing industry in the UK ( and elsewhere in the pooled information). On completion of this work, there will be a better understanding of whether work in the lead industry poses a cancer risk, and if so what exposures give rise to an increased cancer risk. This analysis is part of the most definitive study of work in this industry ever undertaken and the results will influence and inform international occupational health policy makers, including the Health and Safety Executive. The results will have the potential to directly influence the health and safety policies of the lead industry and the actions they will take will have a direct benefit for workers and reduce the burden on the health system.
INTENSIVE CARE NATIONAL AUDIT & RESEARCH CENTRE (ICNARC)	INTENSIVE CARE NATIONAL AUDIT & RESEARCH CENTRE (ICNARC)	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The objective for processing these data are to aid the follow-up of patients in the Psychological Outcomes following a nurse-led Preventative Psychological Intervention for critically ill patients (POPPI) cluster-randomised controlled trial. Data obtained from NHS Digital will be used only to ascertain whether patients taking part in the trial are still alive at six months (the follow-up time point for the POPPI trial). Where ICNARC find out that a patient has passed away, no contact will be made - helping to ensure relatives are not caused undue stress by no longer appropriate contact. The POPPI trial is funded by the National Institute for Health Research Health Services and Delivery Research Programme (funding reference 12/64/124) and is carrying out a clinical and cost-effectiveness evaluation of a nurse-led preventative psychological intervention for patients in intensive care, with the aim of reducing the burden of serious psychological morbidity at six months (which include post-traumatic stress disorder, anxiety and depression). Patients surviving to six months after providing informed consent are sent a follow-up questionnaire (which contains the primary outcome and some secondary outcomes for the trial). Primary outcomes: To evaluate, Patient-reported PTSD symptom severity at six months and Incremental costs, quality adjusted life years and net monetary benefit Secondary outcomes: To compare: Days alive and free from sedation to day 30, Duration of critical care unit stay and Depression at six months. Post traumatic Diagnostic Scale score of greater than 18 points at six months and Health-related quality of life at six months	Patients providing informed consent to take part in the POPPI trial have agreed for identifiable information to be collected about them. For patients reaching six months in the trial and who are believed to be alive, their date of birth, postcode, NHS number and patient ID number will be provided to NHS Digital. NHS Digital will then link these identifiers to national records and send back a spread sheet confirming the latest status for each patient, and if relevant, the date of latest posting. The General Practice (GP) code field has been requested to facilitate the follow-up of patients in the trial. Where a patient has not responded to the follow-up questionnaire, the POPPI trial team will contact the patients GP practice to confirm or update contact details. The GP code will be used by the POPPI trial team to enable the patient’s GP practice to be identified rapidly, and ensure follow-up is completed timely. Updated data will then be added to the secure POPPI trial database to ensure no contact is attempted with patients who have passed away. Data collected from NHS Digital will be used only by a limited number authorised individuals in the POPPI trial team who are employed by ICNARC with a legitimate need to use the data (i.e. sending the questionnaires and conducting analysis of the data). Only substantive employees of ICNARC will access the data supplied by NHS Digital. Outputs from the study will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. Red Technology UK and Disaster Recovery UK employees will not access the data. ICNARC will act to preserve patient confidentiality and will not disclose or reproduce any information by which patients could be identified. Data will not be used for commercial purposes, provided in record level form to any third party, and not used for direct marketing.	The output for the purpose of this request will be the cohort dataset with information about who has passed away and details of the GP practice for all members. This will then be used to enable the appropriate follow up questionnaire to be sent out to all living participants of the POPPI Trial. For the POPPI trail itself the team will prepare and submit a report to the funder - National Institute for Health Research (NIHR) Health Services and Delivery Research Programme. Clinical trials funded by the NIHR are published in the open-access (free of charge) NIHR Journal's Library (http://www.journalslibrary.nihr.ac.uk/), meaning the results can be accessed by patients, carers, clinicians and researchers alike. The estimated publication date for the NIHR report is March 2018. Articles will also be submitted to relevant scientific journals (e.g. medical journals and psychology journals). It will not be possible to identify any person who has taken part in the study in any reports or articles. The results of the POPPI trial will be both widely and actively disseminated. A full detailed report of the POPPI trial will be submitted to the National Institute for Health Research for publication in the peer reviewed, open access Health Services and Delivery Research Programme journal (due to be published in March 2018). The primary results will also be submitted for publication in March 2018 in an high-impact, widely-read, general medical journal, such as the New England Journal of Medicine (this is where the last two large ICNARC trials have also been published). In addition, the results of the POPPI trial will be presented at: regional critical care network meetings; national professional conferences; the Annual Meeting of the ICNARC Case Mix Programme; the Annual Meeting of the UK Critical Care Research Forum; and national and international critical care and clinical and health psychology conferences/meetings. This dissemination plan will ensure that the results of the trial are fed back to those delivering and organising care (e.g. nurses, doctors, managers) in the NHS (and across the world), allowing for any learning from the trial to influence clinical practice for the benefit of critically ill patients. The trial results will also be available to patients and the l public via the ICNARC website (www.icnarc.org) and a press release. [Note: It will not be possible to identify any individual participating patient in any trial reports or presentations].	The main benefit for the data being requested is to ensure that no further contact is made by the POPPI trail with participants who have passed away helping to ensure relatives are not caused undue stress by no longer appropriate contact. Studies indicate high rates of serious psychological morbidity (e.g. post-traumatic stress disorder, anxiety and depression) amongst patients after their stay in a critical care unit. Early psychological assessment of risk and subsequent intervention/support are both key to reduce longer-term psychological morbidity. The Psychological Outcomes following a nurse-led Preventative Psychological Intervention for critically ill patients (POPPI) cluster-randomised controlled trial sets out to inform the NHS on improving both access to, and delivery of, services to ensure that critically ill patients receive both psychological assessment and intervention/support in a cost-effective manner. The POPPI complex intervention includes creating a more therapeutic environment for patients in the critical care unit, assessing consenting patients for acute psychological stress and, for those identified as acutely stressed, delivering three one to one stress support sessions (which are delivered by a specially trained POPPI nurse). It is not yet known whether this intervention is beneficial for patients (this is what the trial will determine) or cost-effective for the NHS, but if this intervention is found to be clinically and cost-effective, the results of the trial will have a high impact on critical care services in the UK and internationally, particularly as there is no current routine care pathway to address the psychological morbidity of critical care patients in the UK. The primary results of the trial are estimated to be published in March 2018.
KPMG LLP	KPMG LLP	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
KPMG LLP	KPMG LLP	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Purpose - Overall Context The National Cancer Vanguard covers three systems: • Greater Manchester (led by The Christie NHS Foundation Trust) • North West and South West London (led by The Royal Marsden NHSFT) • West Essex, North Central and North East London (led by University College London Hospitals NHS Foundation Trust) Note that the University College London Hospitals NHS Foundation Trust will not be permitted to access any data under this agreement. The vanguard has been tasked with radically restructuring cancer care systems to place the patient at the heart of service planning and delivery. The case for change has been clearly highlighted in the NHS Five Year Forward View, through the three gaps, namely: • Health and Wellbeing Gap – 1 in 2 people will get cancer in their lifetime. The incidence of cancer is increasing by about 2% per year and is the biggest cause of death from any disease in every age group. The shift in lifestyle in increasing the age standardised risk of cancer. • Care and Quality Gap – cancer patients are diagnosed too late, survival is poor, cancer is not prevented where it can be and living with and beyond cancer is not consistently prioritised. Patients receive inconsistent quality of care, long waiting times, widely varying outcomes and often poor experience. • Funding Gap – the cost of delivering the capacity required to bring forward diagnosis and shift follow up care into the community and other settings is currently prohibitive. To tackle these gaps, the vanguard has a programme of works which fall under three categories: • Transforming the clinical model of delivery refocusing funding across cancer pathways away from costly specialist treatment of late stage cancers to prevention and diagnosis and reducing unwarranted variation – consistently applying best evidence based practice including access to 24/7 end of life and palliative care and supporting patients living with and beyond cancer; • Changing the system architecture by: - creating sector-wide single cancer budgets and lead provider models within the context of a system leader, underpinned by financial incentives that transform cancer care; - developing robust governance models, supported by appropriate organisational form, that drive shared accountability across system; and - working with commissioner colleagues to radically reform and strengthen commissioning processes in order to streamline accountabilities and drive forward service improvement. • Implementing enabling infrastructure to include outcomes measurement and shared reporting co-created with patients / carers and clinicians supported by shared MDT level balanced scorecards, data capture standards, and analytical capabilities to drive best practice evidence based decision making and outcomes commissioning. Purpose - Specific work streams requiring data from NHS Digital Through the programme of works noted above, there are three key purposes of why data is needed, which fall under two main work streams: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data] a) Scoping analytics: When determining the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model we will need to perform comprehensive analysis to understand the current population and existing pathways. b) Model building: Once we have decided on a payment mechanism and the patient cohort and pathways covered by the new commissioning model, we will need to build a financial and activity model. This will need to project the impact of the new commissioning model by the various organisations impacted, and be able to calculate payments. This model would also be used to form the basis of contracts over a multiple year period. 2) To perform pan-vanguard data analytics to aid decision making (implementing enabling infrastructure) [HES data via HDIS] a) Performance reports: This will include production of comparable metrics the Vanguard providers of cancer care including NHS Providers and CCGs. The aim of these comparable analyses will be identify areas for improvement overall within the Vanguard, or system within the Vanguard, and also look for areas of variation. This will then, be used to inform the work programme of the Vanguard to improve patient’s cancer care and reduce variation. The learning from this approach will be shared nationally to inform the development of Cancer Alliances. As a Vanguard, there is an expectation that any models/tools built must be replicable so that they could be rolled out nationally. Data: The commissioning model work will rely on SUS data as this is the dataset relating to commissioning payments throughout the NHS. The Data Analytics work will use HES data via the HDIS tool as this permits rapid quantitative analysis without the need to store a large amount of record-level HES data. National data is required rather than just London and Manchester data because cancer patients may travel some distance to receive care in specialist centres, and to permit the development and evaluation of models/tools which can be used across the country rather than limited to specific areas only.	Only substantive employees of the Data Controllers (The Royal Marsden NHS Foundation Trust and The Christie NHS Foundation Trust), and the Data Processor (KPMG) will access the data. At no point will any of the data included in this agreement be permitted to be linked with any other record level data, nor will the SUS data be linked to the HES data. Any outputs beyond these substantive employees will contain data only where that data is aggregated with small numbers suppressed in line with the HES Analysis Guide. Note on the role of the Data Processor: KPMG has been engaged to act as a Data Processor for both Data Controllers (Royal Marsden NHSFT and The Christie NHSFT). KPMG’s work is constrained to purpose 1 (the Creation of new commissioning model for cancer services), and will use only SUS data, with no access to HES data. The processing activities support defined purposes as follows: 1) Creation of new commissioning model for cancer services (changing system architecture) [SUS data only] The model and any tools created for this purpose will be developed by the Data Processor and then be delivered to the Data Controllers (The Royal Marsden NHSFT and The Christie NHSFT). The raw SUS, pseudonymised, record-level extracts will be stored in a secure database, which is specifically designed for the purpose with suitable security and administrative controls to govern access. Once the model/tools are developed, the record-level data will be required to allow the model/tools to function correctly. For this reason, the raw SUS (pseudonymised, record-level extracts) will be securely transferred to both The Royal Marsden NHSFT and The Christie NHSFT. Aggregated level database views will be created from the record-level extracts to produce counts of the SUS activity and sum of cost by dimensions such as provider, CCG, age, sex and cancer type. These aggregated database views will then be used to feed data into analysis, model building and performance reporting. Any outputs from the model/tool – that will be shared outside of the Royal Marsden NHS FT and The Christie NHS FT will be aggregated data only (with small numbers suppressed in line with the HES Analysis Guide). The aggregated database views will also be used to feed data into analysis, model building and performance reporting as described below. 1a) Scoping analytics tool This analysis tool will enable the Cancer Vanguards and their provider Trusts and CCGs to understand the current population and existing pathways to determine the population cohort (demographics, location, disease type) and pathways that will be covered by a new payments model. 1b) Model building This will involve the build of a data model to extract the payment mechanism and patient cohort and pathways covered by the current commissioning model (both activity and cost), and calculate the payments for treating those patients under the pathways in scope. The data will be used to count outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. 2) Pan Vanguard data analytics: [using HES data only] The Data Controllers (Royal Marsden NHSFT and The Christie NHSFT) will analyse hospital records (HES data held in HDIS) for this purpose. 2a) Performance reports Quantitative performance reports will be generated using data from the HDIS system. The Royal Marsden NHSFT and The Christie NHSFT are permitted to download aggregated reports (not record-level) from HDIS which contain small numbers. All small numbers will be suppressed in line with the HES Analysis Guide before any reports are shared to any third party (including the Data Processor). These will then be stored on Royal Marsden and The Christie servers. Data will then be analysed using tools such as Excel.	1a) Scoping analytics: The outputs of the scoping analytics will be in the form of aggregated counts of activity for outpatient referrals and attendances, inpatient admissions, and A&E attendances by dimensions such as Provider, CCG, GP Practice, Cancer Type, Age, Ethnicity, Referral Source and Clinical Unit. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 1b) Model building: The outputs of the model building will be in the form of an interactive tool showing aggregated counts of activity and cost for cancer pathways. Where the aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. These aggregated outputs will be shared with providers and CCGs within the 2 Cancer Vanguards. 2a) Performance reports: The analysis of the defined metrics, will feed into the wider Pan-London & Greater Manchester metrics being developed for Cancer Services. As well as tabulated outputs, this also often includes a graphical view of the data along with any key commentary, limitations and also the source of the data. Where aggregated outputs contain small numbers, these will be suppressed in line with the HES Analysis Guide. The specific defined metrics will be developed in conjunction with the Vanguard tumour specific pathway groups. This will include discussion of metrics where outputs have already been produced to establish the frequency any refresh of the data. Illustrative examples of the types of analysis which would be undertaken are- • Counts of numbers of specialist surgical procedure, either limited to cancer diagnosis or split by diagnosis type where the same type of surgery is undertaken for non-cancer diagnosis. • Counts & rates of surgical approach (e.g Open compared to Minimal Access Approach) • Emergency readmission rates within discharge of surgical procedures for a defined number of days (previous national analysis has used 28 or 30 days) • Day case of overnight stay and immediate reconstruction rates for surgical procedures where applicable. For example breast cancer mastectomies. This list of metrics is not exhaustive as it is expected that individual pathway groups will identify additional priorities which may change over time. For all analysis the default position would be to run the analysis for the whole of England, both to enable comparisons with the England rates, and also potentially to share other regional breakdowns of the data to support the introduction of Cancer Alliances nationally. The target date for these outputs is likely to be in early 2017.	The Cancer Vanguard will develop programmes to raise public awareness and work collaboratively with partners in education, health and social care to shift the focus towards prevention and early diagnosis, to provide a recovery package to aid those living with and beyond cancer and to greatly improve care at the end of life. Placing patients at the heart of the work across whole organisational boundaries provides an opportunity to make a real difference in cancer care. The Cancer Vanguard will work with patient groups and patient representatives to ensure that they, their families and carers are meaningfully involved at every stage in shaping how the new system will work. Working together across a whole pathway will make a real difference in the way resources are used, and enable clinicians to provide patients with the best cancer care available anywhere in the world. 1a and 1b) Scoping analytics & Model building: Many of the interventions needed for people affected by cancer are the same as those living with other long term conditions. Commissioners should take this into account by commissioning interventions required by the individual rather than dealing with the cancer in isolation. The commissioning and provision of services to support people affected by cancer may or may not need to be cancer specific but does need to follow the principles of person centred care as laid out in the NHS England Long Term Conditions Framework. A new model of commissioning would reflect this need. Scoping analytics and model building will form a pivotal part of the new commissioning model. New models of care are a core component of helping the NHS become more sustainable and are key in the system delivering the aims of the Five Year Forward View. Specifically, the key benefits of a new cancer commissioning model are: • Being able to link payment for cancer services to outcomes that are in the best interest of patients (such as improving experience, quality of life and clinical outcomes), rather than the current system which pays for services as inputs and outputs. • Using the commissioning model to optimise pathways, by incentivising collaboration between providers and reducing duplication in care. This will carry a financial saving to the system but also improve patient experience. • Enabling commissioning for cancer to be less fragmented than the current system which can help prioritise key areas for investment and enable longer term planning. 2a) Performance reports: In October 2014 the NHS in England published its strategy for the next five years (the Five Year Forward View). This strategy made it clear that new ways of organising NHS care would need to be developed in the coming years to meet the challenges faced by the NHS. In the light of this strategy all NHS organisations were asked to put themselves forward to test some of these new ways of organising care (as so-called vanguards). At the same time, an independent cancer taskforce appointed by the NHS was publishing its recommendations, which included that a new way of providing cancer care under a single lead organisation for an entire region should be tested. The production of comparative metrics across London & Greater Manchester will enable the identification of areas which need improvement across a system, and also those areas within a system with large variation. This will then influence the priorities and service improvements which Vanguard implements, which will in turn lead to a reduction in variation and improved cancer patient care. As well as system led change the work is also expected to influence improvement within individual providers of cancer care, given that the benchmarked outputs will be shared with NHS stakeholders across the cancer pathway. Previous experience with other data sources has indicated that this type of approach facilitates local improvement as it highlights where a particular provider is performing relatively badly compared to other similar providers. In addition the methodology for any work undertaken by the Cancer Vanguard can be shared with the emerging Cancer Alliances nationally meaning the benefit of this work should be seen nationally. The benefits should start to be seen during the financial year 2017-2018, onwards.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses.	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Only third parties as specified above will be given access to the signals from noise (sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. The patient level data does not leave the Lightfoot secure hosting facility, and the clients’ analysts do not have access to record level data. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressions. Lightfoot agree a senior customer representative with each client to approve all client user requests. Lightfoot provide customers with a process for administration of users. All clients’ user access requests are authorised by the Lightfoot appointed account manager who is responsible for checking the user access is in accordance with license agreements and HES analysis guidelines. The process for user access management has been revised in line with the recommendations in the “HSCIC audit of data sharing activities” report 15/07/2016. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool.	The following are examples of benefits achieved through use of the sfn tools with Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases a narrative around expected benefits has been provided and a metrics strategy to quantify benefits will be part of the work streams as the approach uses HES data with SPC for evidence based change to health services. 1) South West Academic Health Science Network with NIHR CLAHRC South West Peninsula on behalf of NHS England have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). This was testing a new approach to QOF findings which would allow clinical freedom to innovate while continuing to provide high quality care. Specifically the data were used to monitor non-elective emergency admission rates for SPQS practices for MI, Stoke, COPD, and diabetes as a partial indicator for patient outcomes to evaluate the two approaches. The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS, this will help understanding to establish markers for improved patient outcomes. http://www.swahsn.com/wp-content/uploads/2016/06/Evaluation-of-the-Somerset-Practice-Quality-Scheme-July-2015.pdf ) 2) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes . The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. This work is likely to lead to more centralised care that is evidence based to improve patient outcomes. It is hoped the findings will inform thinking when developing the ambitions for the delivery of seven day services and access and treatment to specialist services within developing Sustainability and Transformation Plans (STPs). HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. This paper makes recommendations for service re-configuration across the South West of England that will deliver the highest clinical benefit in terms of time to emergency centre for the local population. http://www.swscn.org.uk/delivering-five-year-forward-view-transforming-cardiovascular-disease-services-deliver-four-priority-clinical-standards-specialist-services/7939/ 3) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) are conducting research to look at number and location of neonatal and childbirth centres in England. The HES data for childbirth was used for the modelling. That is funded by a National Institute for Health Research (NIHR) grant: http://www.nets.nihr.ac.uk/projects/hsdr/141908. The aim of this proposed research is to understand national neonatal care demand and to investigate configurations of service that best meet the needs of both service providers and parents. The publication date is September 2017. 4) South West Academic Health Science Network have used the platform to assess the opportunity for social intervention bonds around diabetes activity and alcohol related activity for early intervention in terms of Emergency Department Care, Outpatient Care and Admitted Patient Care. 5) An NHS Clinical Commissioning organisation has identified patients attending A&E and subsequently admitted whose initial diagnoses were considered an ambulatory care sensitive condition. This allowed commissioners to identify patient pathways to be reviewed with secondary care providers. These are long term projects are currently been scoped and the CCG view the use of HES data through Lightfoot’s SPC tool as vital to been able to monitor and evidence the success of this work. The work streams have not been implemented yet so we are unable to quantify the patient benefits and provide dates. The benefits of using HES with SPC methods include the timeliness of the findings. In many cases the length of time to undertake a full summative evaluation is simply too long so interim measures of progress that are statistically robust are needed. This will influence direction of travel to identify change in a way that allows clients to respond – reinforcing the good and reacting to the bad - within a shorter timeframe to optimise transformation activity. SPC methods make more use of the information available by looking not just at one point in time but considering the history of observations. They can be adapted to look at change against a variety of benchmarks, could set the expectations to be an improvement on historical patterns or to be better than elsewhere or to change faster than elsewhere. So, for example, it is possible to test first whether in a specific area there is an improvement over time, and then test to see if this change is greater or lesser than that seen elsewhere. (Nuffield 2016 “Monitoring change in health care through SPC methods.”) The following work streams will start in the next couple of months. Lightfoot will agree a metric strategy with the work streams. i) A review of outpatient pathways for cardiology, gastro and respiratory to improve DNA rates and reduce waiting times. SPC using HES will be used to track improvement over time and compare if change is greater or lesser than elsewhere. ii) Work with the acute provider clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. Lightfoot expect the use of HES data to show an impact on outpatient activity and a reduction in acute admissions for the frail elderly. These metrics and timeline will be agreed as part of the project. SPC methods will aid commissioners to forecast the impact of initiatives on activity to assist with planning health provision. 6) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes. This work has progressed for one ambulance service which has used the HES data to demonstrate to stakeholders and commissioners the effectiveness of a new clinical model in a particular geographical area. The HES data quantified the new model of care in terms of the contribution of the ambulance service to the wider health system in order support the case for further pathway re-design through development of hear and treat and see and treat models of care. HES data showed the deployment of paramedics practitioners in the new model of care had significant impact on reducing ambulance attendances by an annual reduction of 11,190, this model delivers a better quality of service to patients who are treated at home instead of conveyed to a hospital. The trust is exploring a phased approach to establishing further initiatives for service redesign leading to reduced hospital transports and more care nearer the home in the community. The ambulance service views the on-going use of HES data as essential to monitor the success of these initiatives to provide evidence of change in discussions with commissioners and other providers. 7) Lightfoot continue to provide benchmarking data to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. A. Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model which allowed increased rates of See and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 7) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. The SFN took allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. B. Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. C. Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses.	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Only third parties as specified above will be given access to the signals from noise (sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. The patient level data does not leave the Lightfoot secure hosting facility, and the clients’ analysts do not have access to record level data. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressions. Lightfoot agree a senior customer representative with each client to approve all client user requests. Lightfoot provide customers with a process for administration of users. All clients’ user access requests are authorised by the Lightfoot appointed account manager who is responsible for checking the user access is in accordance with license agreements and HES analysis guidelines. The process for user access management has been revised in line with the recommendations in the “HSCIC audit of data sharing activities” report 15/07/2016. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool.	The following are examples of benefits achieved through use of the sfn tools with Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases a narrative around expected benefits has been provided and a metrics strategy to quantify benefits will be part of the work streams as the approach uses HES data with SPC for evidence based change to health services. 1) South West Academic Health Science Network with NIHR CLAHRC South West Peninsula on behalf of NHS England have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). This was testing a new approach to QOF findings which would allow clinical freedom to innovate while continuing to provide high quality care. Specifically the data were used to monitor non-elective emergency admission rates for SPQS practices for MI, Stoke, COPD, and diabetes as a partial indicator for patient outcomes to evaluate the two approaches. The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS, this will help understanding to establish markers for improved patient outcomes. http://www.swahsn.com/wp-content/uploads/2016/06/Evaluation-of-the-Somerset-Practice-Quality-Scheme-July-2015.pdf ) 2) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes . The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. This work is likely to lead to more centralised care that is evidence based to improve patient outcomes. It is hoped the findings will inform thinking when developing the ambitions for the delivery of seven day services and access and treatment to specialist services within developing Sustainability and Transformation Plans (STPs). HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. This paper makes recommendations for service re-configuration across the South West of England that will deliver the highest clinical benefit in terms of time to emergency centre for the local population. http://www.swscn.org.uk/delivering-five-year-forward-view-transforming-cardiovascular-disease-services-deliver-four-priority-clinical-standards-specialist-services/7939/ 3) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) are conducting research to look at number and location of neonatal and childbirth centres in England. The HES data for childbirth was used for the modelling. That is funded by a National Institute for Health Research (NIHR) grant: http://www.nets.nihr.ac.uk/projects/hsdr/141908. The aim of this proposed research is to understand national neonatal care demand and to investigate configurations of service that best meet the needs of both service providers and parents. The publication date is September 2017. 4) South West Academic Health Science Network have used the platform to assess the opportunity for social intervention bonds around diabetes activity and alcohol related activity for early intervention in terms of Emergency Department Care, Outpatient Care and Admitted Patient Care. 5) An NHS Clinical Commissioning organisation has identified patients attending A&E and subsequently admitted whose initial diagnoses were considered an ambulatory care sensitive condition. This allowed commissioners to identify patient pathways to be reviewed with secondary care providers. These are long term projects are currently been scoped and the CCG view the use of HES data through Lightfoot’s SPC tool as vital to been able to monitor and evidence the success of this work. The work streams have not been implemented yet so we are unable to quantify the patient benefits and provide dates. The benefits of using HES with SPC methods include the timeliness of the findings. In many cases the length of time to undertake a full summative evaluation is simply too long so interim measures of progress that are statistically robust are needed. This will influence direction of travel to identify change in a way that allows clients to respond – reinforcing the good and reacting to the bad - within a shorter timeframe to optimise transformation activity. SPC methods make more use of the information available by looking not just at one point in time but considering the history of observations. They can be adapted to look at change against a variety of benchmarks, could set the expectations to be an improvement on historical patterns or to be better than elsewhere or to change faster than elsewhere. So, for example, it is possible to test first whether in a specific area there is an improvement over time, and then test to see if this change is greater or lesser than that seen elsewhere. (Nuffield 2016 “Monitoring change in health care through SPC methods.”) The following work streams will start in the next couple of months. Lightfoot will agree a metric strategy with the work streams. i) A review of outpatient pathways for cardiology, gastro and respiratory to improve DNA rates and reduce waiting times. SPC using HES will be used to track improvement over time and compare if change is greater or lesser than elsewhere. ii) Work with the acute provider clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. Lightfoot expect the use of HES data to show an impact on outpatient activity and a reduction in acute admissions for the frail elderly. These metrics and timeline will be agreed as part of the project. SPC methods will aid commissioners to forecast the impact of initiatives on activity to assist with planning health provision. 6) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes. This work has progressed for one ambulance service which has used the HES data to demonstrate to stakeholders and commissioners the effectiveness of a new clinical model in a particular geographical area. The HES data quantified the new model of care in terms of the contribution of the ambulance service to the wider health system in order support the case for further pathway re-design through development of hear and treat and see and treat models of care. HES data showed the deployment of paramedics practitioners in the new model of care had significant impact on reducing ambulance attendances by an annual reduction of 11,190, this model delivers a better quality of service to patients who are treated at home instead of conveyed to a hospital. The trust is exploring a phased approach to establishing further initiatives for service redesign leading to reduced hospital transports and more care nearer the home in the community. The ambulance service views the on-going use of HES data as essential to monitor the success of these initiatives to provide evidence of change in discussions with commissioners and other providers. 7) Lightfoot continue to provide benchmarking data to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. A. Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model which allowed increased rates of See and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 7) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. The SFN took allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. B. Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. C. Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses.	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Only third parties as specified above will be given access to the signals from noise (sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. The patient level data does not leave the Lightfoot secure hosting facility, and the clients’ analysts do not have access to record level data. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressions. Lightfoot agree a senior customer representative with each client to approve all client user requests. Lightfoot provide customers with a process for administration of users. All clients’ user access requests are authorised by the Lightfoot appointed account manager who is responsible for checking the user access is in accordance with license agreements and HES analysis guidelines. The process for user access management has been revised in line with the recommendations in the “HSCIC audit of data sharing activities” report 15/07/2016. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool.	The following are examples of benefits achieved through use of the sfn tools with Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases a narrative around expected benefits has been provided and a metrics strategy to quantify benefits will be part of the work streams as the approach uses HES data with SPC for evidence based change to health services. 1) South West Academic Health Science Network with NIHR CLAHRC South West Peninsula on behalf of NHS England have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). This was testing a new approach to QOF findings which would allow clinical freedom to innovate while continuing to provide high quality care. Specifically the data were used to monitor non-elective emergency admission rates for SPQS practices for MI, Stoke, COPD, and diabetes as a partial indicator for patient outcomes to evaluate the two approaches. The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS, this will help understanding to establish markers for improved patient outcomes. http://www.swahsn.com/wp-content/uploads/2016/06/Evaluation-of-the-Somerset-Practice-Quality-Scheme-July-2015.pdf ) 2) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes . The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. This work is likely to lead to more centralised care that is evidence based to improve patient outcomes. It is hoped the findings will inform thinking when developing the ambitions for the delivery of seven day services and access and treatment to specialist services within developing Sustainability and Transformation Plans (STPs). HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. This paper makes recommendations for service re-configuration across the South West of England that will deliver the highest clinical benefit in terms of time to emergency centre for the local population. http://www.swscn.org.uk/delivering-five-year-forward-view-transforming-cardiovascular-disease-services-deliver-four-priority-clinical-standards-specialist-services/7939/ 3) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) are conducting research to look at number and location of neonatal and childbirth centres in England. The HES data for childbirth was used for the modelling. That is funded by a National Institute for Health Research (NIHR) grant: http://www.nets.nihr.ac.uk/projects/hsdr/141908. The aim of this proposed research is to understand national neonatal care demand and to investigate configurations of service that best meet the needs of both service providers and parents. The publication date is September 2017. 4) South West Academic Health Science Network have used the platform to assess the opportunity for social intervention bonds around diabetes activity and alcohol related activity for early intervention in terms of Emergency Department Care, Outpatient Care and Admitted Patient Care. 5) An NHS Clinical Commissioning organisation has identified patients attending A&E and subsequently admitted whose initial diagnoses were considered an ambulatory care sensitive condition. This allowed commissioners to identify patient pathways to be reviewed with secondary care providers. These are long term projects are currently been scoped and the CCG view the use of HES data through Lightfoot’s SPC tool as vital to been able to monitor and evidence the success of this work. The work streams have not been implemented yet so we are unable to quantify the patient benefits and provide dates. The benefits of using HES with SPC methods include the timeliness of the findings. In many cases the length of time to undertake a full summative evaluation is simply too long so interim measures of progress that are statistically robust are needed. This will influence direction of travel to identify change in a way that allows clients to respond – reinforcing the good and reacting to the bad - within a shorter timeframe to optimise transformation activity. SPC methods make more use of the information available by looking not just at one point in time but considering the history of observations. They can be adapted to look at change against a variety of benchmarks, could set the expectations to be an improvement on historical patterns or to be better than elsewhere or to change faster than elsewhere. So, for example, it is possible to test first whether in a specific area there is an improvement over time, and then test to see if this change is greater or lesser than that seen elsewhere. (Nuffield 2016 “Monitoring change in health care through SPC methods.”) The following work streams will start in the next couple of months. Lightfoot will agree a metric strategy with the work streams. i) A review of outpatient pathways for cardiology, gastro and respiratory to improve DNA rates and reduce waiting times. SPC using HES will be used to track improvement over time and compare if change is greater or lesser than elsewhere. ii) Work with the acute provider clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. Lightfoot expect the use of HES data to show an impact on outpatient activity and a reduction in acute admissions for the frail elderly. These metrics and timeline will be agreed as part of the project. SPC methods will aid commissioners to forecast the impact of initiatives on activity to assist with planning health provision. 6) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes. This work has progressed for one ambulance service which has used the HES data to demonstrate to stakeholders and commissioners the effectiveness of a new clinical model in a particular geographical area. The HES data quantified the new model of care in terms of the contribution of the ambulance service to the wider health system in order support the case for further pathway re-design through development of hear and treat and see and treat models of care. HES data showed the deployment of paramedics practitioners in the new model of care had significant impact on reducing ambulance attendances by an annual reduction of 11,190, this model delivers a better quality of service to patients who are treated at home instead of conveyed to a hospital. The trust is exploring a phased approach to establishing further initiatives for service redesign leading to reduced hospital transports and more care nearer the home in the community. The ambulance service views the on-going use of HES data as essential to monitor the success of these initiatives to provide evidence of change in discussions with commissioners and other providers. 7) Lightfoot continue to provide benchmarking data to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. A. Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model which allowed increased rates of See and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 7) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. The SFN took allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. B. Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. C. Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot are an organisation who work to help healthcare organisations transition from a traditional silo based structure to a flow-based system-wide management approach. By incorporating data from different healthcare providers, they are able to measure patient outcomes across the whole pathway, linking all of the services in each patient’s journey. Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. The platform uses statistical process control techniques that relies on time series data, this is used to assess if patient waiting time or length of stay is increasing or decreasing and assessing if an improvement initiative has had a statistical impact on agreed metrics to measure improvement by calculating cyclic trends using the time series data. For example, this enables Lightfoot to answer the question have emergency admissions increased due to an unassigned special cause, change to patient pathways or is part of normal seasonal variation. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses. The Lightfoot HES group oversees the governance for approving and on-boarding new clients this group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation or academic organisation alone or working as part of academic science network (AHSN) conducting health research using the data for the provision of health services or promotion of health	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Third parties as specified in this application will only access the data via the signals for noise(sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. No organisation other than Lightfoot are able to access record level data, all third party access which is restricted to the customer group set out in the application are aggregated with small numbers suppressed inline with the HES analysis guide. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, NHS health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressed in line with the HES analysis guide. The process for access control for all users of the tool is; • Before approving or rejecting a request, an email should be sent to helpdesk@Lightfootsolutions.com with details of the request - the environment and level of access. • A corresponding case will be created in CRM by the helpdesk. This should be one case per person contain the name of the person in the subject line. Only ‘bulk’ requests (6+ on a single request) should be added as a single case. • Helpdesk should then inform the Account Manager of the request and seek authorisation for the user(s). • These user requests or User lists need to be validated by the Account Manager against the approval notification sent via email through the helpdesk@Lightfootsolutions.com, with a cc to the lead analyst for that particular client. • All External User requests must be authorised by the nominated client lead and also the Lightfoot Account Manager. • All Internal User requests must be authorised by the Lightfoot Account Manager • Once users are approved, they can be allocated the authorised access. • Before the user is added or access changed , a risk assessment of the appropriate access should be considered in line with sfn environment and HES analysis guidance. • The list of approved users must be updated by the lead analyst.. • Once the user has been issued their username and training/confirmation has taken place, the CRM case can be closed and user list updated. • Internal users should be deleted as part of the “Lightfoot leaver Process” • Client access should be removed on expiration of a contract. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). Data provided by NHS Digital under this agreement will not be linked with any other record level data.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool. All outputs are subject to appropriate suppression of small numbers in line with the HES analysis guide.	The following are examples of benefits achieved through use of the sfn tools with HES data for Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases, a narrative around expected benefits has been provided. 1) South West Academic Health Science Network is continuing the use of sfn tools with HES data to work with NIHR CLAHRC South West Peninsula on behalf of NHS England. They have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS. The HES data continues to support the roll out of this work. 2) In 2017 South West Academic network used the HES data to support a southwest acute trust to understand the urgent care flow through their acute hospital. HES within the sfn platform was used for a quick drill down to understand why waiting time in ED had increased. The organisations thought it was changes to demand due to changes to OOHs providers. They looked at the flow through the system and were able to isolate changes to flows in the hospital as a root cause. Their A&E delivery board used the information to myth bust and pinpoint areas to reduce patient waiting time using the data. 3) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes. The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. The proposal for service re-configuration is going through the governance system and a reconfiguration is expected. 4) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) used HES data over the past year to: • Help Torbay and Devon Foundation trust with understanding the number of beds needed to obtain a good flow of patients through the acute and rehabilitation unit stroke care pathway. The aim is that patients should not be held up at any phase of the care pathway due to lack of beds in the next phase. The model’s findings have been presented to Torbay and South Devon NHS Foundation Trust in a report describing how the resources available affect the Trust’s ability to meet best-practice targets for stroke care. (http:/ninsula.nihr.ac.uk/research/penchord-torbay-and-south-devon-stroke-care) • Worked with The Royal Cornwall Hospitals NHS Trust to explore the current Cornwall acute and rehab stroke treatment system, and to determine whether ring-fencing beds in acute hospitals and RSUs would allow those requiring treatment to have a rehab bed in the location of their choice. The team concluded that current rehab bed availability does not match the home location of patients and ‘Ring-fencing’ stroke beds to ensure a free bed is available at a patients closest hospital 90% of the time, would require acute hospitals to run at ~70% average bed occupancy on their stroke wards and Stroke Rehabilitation Units to run at ~75% average bed occupancy. Initial results from the project have been shared with stakeholders via the local Stroke Partnership Board. The results and implications of the work will be reviewed from a commissioning perspective, and will be further shared with Plymouth colleagues, so that learning can be disseminated across the areas studied. (http://clahrc-peninsula.nihr.ac.uk/research/penchord-cornwall-acute-and-community-stroke-bed-capacity-modelling) 5) Lightfoot worked with an acute provider and their commissioner clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. The Trust have used the HES data to consider definition of outcome measures for their acute frailty pathways and want to explore collecting data on patient pathway using their own data. The HES data helped demonstrate concepts of frequent data as part of a continuous improvement framework that will require a daily data feed. 6) Lightfoot are a planning a workshop with Greater Manchester senior leadership to review the incidence of fractured neck of femur across their CCGs and explore using acute data as part of a group of metrics to measure outcomes for their elderly population to monitor initiatives to be rolled out in line with NICE guidance for frail patients and patients with long term conditions. The HES data in the sfn platform will provide data on numbers to support these discussions. 7) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes 8) Lightfoot continue to provide benchmarking data form HES to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. • Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model, which allowed increased rates of See, and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 8) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. Using HEs data this way allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. • Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. • Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot are an organisation who work to help healthcare organisations transition from a traditional silo based structure to a flow-based system-wide management approach. By incorporating data from different healthcare providers, they are able to measure patient outcomes across the whole pathway, linking all of the services in each patient’s journey. Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. The platform uses statistical process control techniques that relies on time series data, this is used to assess if patient waiting time or length of stay is increasing or decreasing and assessing if an improvement initiative has had a statistical impact on agreed metrics to measure improvement by calculating cyclic trends using the time series data. For example, this enables Lightfoot to answer the question have emergency admissions increased due to an unassigned special cause, change to patient pathways or is part of normal seasonal variation. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses. The Lightfoot HES group oversees the governance for approving and on-boarding new clients this group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation or academic organisation alone or working as part of academic science network (AHSN) conducting health research using the data for the provision of health services or promotion of health	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Third parties as specified in this application will only access the data via the signals for noise(sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. No organisation other than Lightfoot are able to access record level data, all third party access which is restricted to the customer group set out in the application are aggregated with small numbers suppressed inline with the HES analysis guide. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, NHS health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressed in line with the HES analysis guide. The process for access control for all users of the tool is; • Before approving or rejecting a request, an email should be sent to helpdesk@Lightfootsolutions.com with details of the request - the environment and level of access. • A corresponding case will be created in CRM by the helpdesk. This should be one case per person contain the name of the person in the subject line. Only ‘bulk’ requests (6+ on a single request) should be added as a single case. • Helpdesk should then inform the Account Manager of the request and seek authorisation for the user(s). • These user requests or User lists need to be validated by the Account Manager against the approval notification sent via email through the helpdesk@Lightfootsolutions.com, with a cc to the lead analyst for that particular client. • All External User requests must be authorised by the nominated client lead and also the Lightfoot Account Manager. • All Internal User requests must be authorised by the Lightfoot Account Manager • Once users are approved, they can be allocated the authorised access. • Before the user is added or access changed , a risk assessment of the appropriate access should be considered in line with sfn environment and HES analysis guidance. • The list of approved users must be updated by the lead analyst.. • Once the user has been issued their username and training/confirmation has taken place, the CRM case can be closed and user list updated. • Internal users should be deleted as part of the “Lightfoot leaver Process” • Client access should be removed on expiration of a contract. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). Data provided by NHS Digital under this agreement will not be linked with any other record level data.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool. All outputs are subject to appropriate suppression of small numbers in line with the HES analysis guide.	The following are examples of benefits achieved through use of the sfn tools with HES data for Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases, a narrative around expected benefits has been provided. 1) South West Academic Health Science Network is continuing the use of sfn tools with HES data to work with NIHR CLAHRC South West Peninsula on behalf of NHS England. They have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS. The HES data continues to support the roll out of this work. 2) In 2017 South West Academic network used the HES data to support a southwest acute trust to understand the urgent care flow through their acute hospital. HES within the sfn platform was used for a quick drill down to understand why waiting time in ED had increased. The organisations thought it was changes to demand due to changes to OOHs providers. They looked at the flow through the system and were able to isolate changes to flows in the hospital as a root cause. Their A&E delivery board used the information to myth bust and pinpoint areas to reduce patient waiting time using the data. 3) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes. The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. The proposal for service re-configuration is going through the governance system and a reconfiguration is expected. 4) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) used HES data over the past year to: • Help Torbay and Devon Foundation trust with understanding the number of beds needed to obtain a good flow of patients through the acute and rehabilitation unit stroke care pathway. The aim is that patients should not be held up at any phase of the care pathway due to lack of beds in the next phase. The model’s findings have been presented to Torbay and South Devon NHS Foundation Trust in a report describing how the resources available affect the Trust’s ability to meet best-practice targets for stroke care. (http:/ninsula.nihr.ac.uk/research/penchord-torbay-and-south-devon-stroke-care) • Worked with The Royal Cornwall Hospitals NHS Trust to explore the current Cornwall acute and rehab stroke treatment system, and to determine whether ring-fencing beds in acute hospitals and RSUs would allow those requiring treatment to have a rehab bed in the location of their choice. The team concluded that current rehab bed availability does not match the home location of patients and ‘Ring-fencing’ stroke beds to ensure a free bed is available at a patients closest hospital 90% of the time, would require acute hospitals to run at ~70% average bed occupancy on their stroke wards and Stroke Rehabilitation Units to run at ~75% average bed occupancy. Initial results from the project have been shared with stakeholders via the local Stroke Partnership Board. The results and implications of the work will be reviewed from a commissioning perspective, and will be further shared with Plymouth colleagues, so that learning can be disseminated across the areas studied. (http://clahrc-peninsula.nihr.ac.uk/research/penchord-cornwall-acute-and-community-stroke-bed-capacity-modelling) 5) Lightfoot worked with an acute provider and their commissioner clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. The Trust have used the HES data to consider definition of outcome measures for their acute frailty pathways and want to explore collecting data on patient pathway using their own data. The HES data helped demonstrate concepts of frequent data as part of a continuous improvement framework that will require a daily data feed. 6) Lightfoot are a planning a workshop with Greater Manchester senior leadership to review the incidence of fractured neck of femur across their CCGs and explore using acute data as part of a group of metrics to measure outcomes for their elderly population to monitor initiatives to be rolled out in line with NICE guidance for frail patients and patients with long term conditions. The HES data in the sfn platform will provide data on numbers to support these discussions. 7) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes 8) Lightfoot continue to provide benchmarking data form HES to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. • Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model, which allowed increased rates of See, and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 8) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. Using HEs data this way allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. • Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. • Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LIGHTFOOT SOLUTIONS UK LTD	LIGHTFOOT SOLUTIONS UK LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Lightfoot are an organisation who work to help healthcare organisations transition from a traditional silo based structure to a flow-based system-wide management approach. By incorporating data from different healthcare providers, they are able to measure patient outcomes across the whole pathway, linking all of the services in each patient’s journey. Lightfoot provide the Signals from Noise (sfn) statistical tool, which is used by or for (where Lightfoot are providing the service) the following non-commercial organisations: NHS (Providers, Commissioners), Exeter Medical School, NHS Professional Associations, or Academic Health Science Networks (AHSNs). The sfn tool is used for the following purposes: 1. Providing access to summary and statistical analysis of patient data to customers with the objective of supporting a greater understanding of patient activity and flow to support the following activities in order to improve health provision: a. Viewing current patient pathways to identify the key constraints and points for improvement, supporting the opportunities for sharing of best practice between clinicians and providers; b. Agreeing with clinicians work plans to address the key constraints identified in the patient pathways causing delays to patients; c. Monitoring and evaluating the impact of the improvement actions; d. Identifying and embedding the improvements and realising the benefits. 2) Providing access to summary and statistical analysis of patient data to NHS commissioning organisations to support healthcare planning and service redesign, using the Statistical Process Control (SPC) view to: a. Provide a view of current patient pathways and to identify key constraints, variation and bottlenecks in the various patient pathways; b. Monitor and evaluate the impact of the improvement actions. 3) Providing access to summary and statistical analysis of patient data to Ambulance Trusts to support service improvement programmes. 4) Providing access to summary and statistical analysis of patient data to the Association of Ambulance Chief Executives (AACE) to enable and support national improvement programmes by using HES data to demonstrate outcomes of patient cohorts taken to hospital via the Urgent & Emergency Care pathway and conveyed by ambulance. Lightfoot also support AACE’s national objective of improved benchmarking between the 10 ambulance trusts in England. The aim being to identify, using HES data, areas of good clinical practice between the 10 ambulance trusts and provide comparison KPIs. In addition the data is used to analyse variation across the region with the aim of identifying best practice and also areas of opportunity. Once the areas of best practice have been identified these can then be spread across the region to improve the health outcomes for the regional and national populations of the England. The platform uses statistical process control techniques that relies on time series data, this is used to assess if patient waiting time or length of stay is increasing or decreasing and assessing if an improvement initiative has had a statistical impact on agreed metrics to measure improvement by calculating cyclic trends using the time series data. For example, this enables Lightfoot to answer the question have emergency admissions increased due to an unassigned special cause, change to patient pathways or is part of normal seasonal variation. Work streams are provided with Statistical Process Control (SPC) view of current pathways for patients and to identify key constraints, variation and bottlenecks in the various patient pathways which is used to monitor and evaluate the impact of the improvement actions and advise when necessary intervention should take place. Data is used to review workforce planning with clinicians to address the key constraints identified in the pathways where there are patient queues due to a mismatch between demand and capacity. Statistical process control identifies and allows clinicians to embed improvements and monitor them in real time using metrics linked to patient quality. In all cases data is for use by operational staff and clinicians to support their work by presenting HES data in a unique and highly visual manner through the Signals From Noise (sfn) tool. The data is presented in dashboards, charts, mapping charts and in written reports as required by clients. In addition a client may use sfn SPC charts to demonstrate where improvements to service levels or patient experience can be made. In these cases the results analysed through sfn are used in reports and client case studies. Lightfoot will also provide secure access for agreed analysts to complete their own analysis and prepare a range of reports including a summary dashboard. In all customer use cases statistical analysis and the drill down platform will automatically suppress small numbers before being presented the user. The user will be informed that some data has been restricted due to small numbers. Lightfoot confirm that no record level data will be provided to any third party customers or Lightfoot internal consultants and analysts. In addition the data will not be used for sales or marketing purposes or in compiling tender responses. The Lightfoot HES group oversees the governance for approving and on-boarding new clients this group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation or academic organisation alone or working as part of academic science network (AHSN) conducting health research using the data for the provision of health services or promotion of health	All processing of record level data will take place on hardware and software wholly owned and controlled by Lightfoot. Processing of record level data takes place within the datacentre without data being transferred to or processed using laptops, desktops or networks outside of the datacentre. C4L hosts Lightfoot’s secure rack, servers, power and internet connection within their facility. All access by third parties is through the Signals from Noise (sfn) tool. The connections to this tool are encrypted over SSL and require the end user to authenticate. The sfn tool has a specific processing layer responsible for applying small numbers rules to all charts and tabulations prior to returning the results to the user/third party. To summarise the process - i) HES Data will be downloaded via SEFT to Lightfoot’s secure data centre facility. This data will then be processed on a server with controls specifically designed for processing of sensitive data. These controls include restricted access and physical security managed under ISO27001. The HES files will be processed and loaded into a SQL server database in a format suitable for Lightfoot’s OLAP tool – signals from noise (sfn). Once text based data has been loaded into the SQL database it will be stored on the secure area of the server encrypted using AES 256 bit encryption. Once the loaded database has been reconciled with trusted national reference sources it is promoted to the production server. ii) Third parties as specified in this application will only access the data via the signals for noise(sfn) tool. Lightfoot’s clients benefit from viewing data through the sfn tool because this allows users to run immediate real time queries across several years of HES data. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that supports the use of statistical process control techniques which clients need for the measurement of process performance and provide greater understanding of patient flow and pathways. The sfn tool provides unique views of the data with functionality (not available in traditional reporting tools) that support the use of statistical process control techniques which is needed by Lightfoot for the measurement of process performance and provide greater understanding of patient flow and pathways. For some customers within the customer groups stated above, Lightfoot will provide secure access for analysts to complete their own analysis and prepare reports and summary dashboard using the sfn tool. No organisation other than Lightfoot are able to access record level data, all third party access which is restricted to the customer group set out in the application are aggregated with small numbers suppressed inline with the HES analysis guide. All analysts may only access aggregated data with small number suppression. Lightfoot will maintain a user log and provide full training for all such users. All users must comply with NHS Digital's Hospital Episode Statistics (HES) Analysis Guide. In all cases summary and statistical analysis will be automatically processed to suppress small numbers before being presented. Lightfoot confirm that no record level data will be provided to any third party. The Lightfoot HES group over sees the governance for approving and on-boarding new clients with access to an SFN platform containing HES data. This group is accountable for ensuring new clients requirements are complying with the Health and Social care act 2012 as amended by the care act 2014. Clients are only approved if they are an NHS organisation, NHS health provider or a not for profit academic organisations conducting health research using the data for the provision of health services or promotion of health. Each client has an environment built to an agreed specification in line with their requirements and complying with HES data analysis guidance. Clients only have access to aggregated data with small number suppressed in line with the HES analysis guide. The process for access control for all users of the tool is; • Before approving or rejecting a request, an email should be sent to helpdesk@Lightfootsolutions.com with details of the request - the environment and level of access. • A corresponding case will be created in CRM by the helpdesk. This should be one case per person contain the name of the person in the subject line. Only ‘bulk’ requests (6+ on a single request) should be added as a single case. • Helpdesk should then inform the Account Manager of the request and seek authorisation for the user(s). • These user requests or User lists need to be validated by the Account Manager against the approval notification sent via email through the helpdesk@Lightfootsolutions.com, with a cc to the lead analyst for that particular client. • All External User requests must be authorised by the nominated client lead and also the Lightfoot Account Manager. • All Internal User requests must be authorised by the Lightfoot Account Manager • Once users are approved, they can be allocated the authorised access. • Before the user is added or access changed , a risk assessment of the appropriate access should be considered in line with sfn environment and HES analysis guidance. • The list of approved users must be updated by the lead analyst.. • Once the user has been issued their username and training/confirmation has taken place, the CRM case can be closed and user list updated. • Internal users should be deleted as part of the “Lightfoot leaver Process” • Client access should be removed on expiration of a contract. Lightfoot regularly meets with clients to review use of the data and compliance with license agreements. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data). Data provided by NHS Digital under this agreement will not be linked with any other record level data.	Lightfoot’s offering is designed to support and enable continuous improvement projects in the NHS and allied health care organisations to improve patient outcomes. Specific outputs include: • Charts and graphical representations of data using statistical process control to highlight variation; • Tabulations and summarised data; • Statistical analysis; • Written reports and recommendations for stakeholders based on findings from analysis and supported by Statistical Process Control (SPC) charts from the sfn tool; • SPC signals and alerts indicating processes where behaviour has recently changed. In all cases these are for Lightfoot’s clients to use with operational staff and clinicians to support service improvement work by presenting HES data in a unique and highly visual manner through the sfn tool. The data is presented in dashboards, charts and in written reports as required by clients. In addition a client may require use of sfn SPC charts to demonstrate to commissioners and other NHS organisations or members of the public where improvements to service levels or patient experience have resulted from their initiatives. In these cases the results analysed through sfn are used in reports and client case studies. All charts and reports included in the outputs use aggregate data (small numbers suppressed) in line with the HES Analysis Guide, derived from the HES data and presented via the sfn tool. All outputs are subject to appropriate suppression of small numbers in line with the HES analysis guide.	The following are examples of benefits achieved through use of the sfn tools with HES data for Lightfoot clients. The maturity of current change projects makes it is difficult to quantify benefits and provide dates for all projects at the current time. In these cases, a narrative around expected benefits has been provided. 1) South West Academic Health Science Network is continuing the use of sfn tools with HES data to work with NIHR CLAHRC South West Peninsula on behalf of NHS England. They have used HES data within the Lightfoot platform to compare patient outcomes by comparing the Somerset Practice Quality Scheme (SPQS) with the national Quality & Outcomes Framework (QOF). The paper has been published, “An evaluation of the Somerset Practice Quality Scheme” July 2015. The paper makes recommendations to expand the notion of quality in primary care and provide a way to capture what is happening systemically in a second evaluation of SPQS. The HES data continues to support the roll out of this work. 2) In 2017 South West Academic network used the HES data to support a southwest acute trust to understand the urgent care flow through their acute hospital. HES within the sfn platform was used for a quick drill down to understand why waiting time in ED had increased. The organisations thought it was changes to demand due to changes to OOHs providers. They looked at the flow through the system and were able to isolate changes to flows in the hospital as a root cause. Their A&E delivery board used the information to myth bust and pinpoint areas to reduce patient waiting time using the data. 3) Exeter University supported the South West Cardiovascular Strategic Clinical network (SW SCN) in recommendations to reconfigure existing acute services to establish a network of emergency centres for heart attacks and strokes. The purpose of these centres is to maximise good outcomes though the provision of high quality specialist services that are resilient and sustainable. HES data was used as part of the modelling to develop a clinical benefit measures to look at the number of patients treated for time and volume sensitive conditions of ST elevated MI and stoke where time to treatment is a big factor in patient outcomes and maintaining function. The proposal for service re-configuration is going through the governance system and a reconfiguration is expected. 4) Pen chord (the Peninsula Collaboration for Health Operational Research and Development) part of SW Peninsula CLAHRC (Collaboration for Leadership in Applied Health Research and Care) used HES data over the past year to: • Help Torbay and Devon Foundation trust with understanding the number of beds needed to obtain a good flow of patients through the acute and rehabilitation unit stroke care pathway. The aim is that patients should not be held up at any phase of the care pathway due to lack of beds in the next phase. The model’s findings have been presented to Torbay and South Devon NHS Foundation Trust in a report describing how the resources available affect the Trust’s ability to meet best-practice targets for stroke care. (http:/ninsula.nihr.ac.uk/research/penchord-torbay-and-south-devon-stroke-care) • Worked with The Royal Cornwall Hospitals NHS Trust to explore the current Cornwall acute and rehab stroke treatment system, and to determine whether ring-fencing beds in acute hospitals and RSUs would allow those requiring treatment to have a rehab bed in the location of their choice. The team concluded that current rehab bed availability does not match the home location of patients and ‘Ring-fencing’ stroke beds to ensure a free bed is available at a patients closest hospital 90% of the time, would require acute hospitals to run at ~70% average bed occupancy on their stroke wards and Stroke Rehabilitation Units to run at ~75% average bed occupancy. Initial results from the project have been shared with stakeholders via the local Stroke Partnership Board. The results and implications of the work will be reviewed from a commissioning perspective, and will be further shared with Plymouth colleagues, so that learning can be disseminated across the areas studied. (http://clahrc-peninsula.nihr.ac.uk/research/penchord-cornwall-acute-and-community-stroke-bed-capacity-modelling) 5) Lightfoot worked with an acute provider and their commissioner clinicians to look at frailty pathways and assess if HES data can be used to understand the impact of new frailty clinics in the community. The Trust have used the HES data to consider definition of outcome measures for their acute frailty pathways and want to explore collecting data on patient pathway using their own data. The HES data helped demonstrate concepts of frequent data as part of a continuous improvement framework that will require a daily data feed. 6) Lightfoot are a planning a workshop with Greater Manchester senior leadership to review the incidence of fractured neck of femur across their CCGs and explore using acute data as part of a group of metrics to measure outcomes for their elderly population to monitor initiatives to be rolled out in line with NICE guidance for frail patients and patients with long term conditions. The HES data in the sfn platform will provide data on numbers to support these discussions. 7) Lightfoot have been commissioned to support several ambulance trusts in analysis of patient journey data with ambulance trusts to review clinical models in order to deliver the local urgent care strategy and develop a model to treat more people at home and refer to local community services where appropriate. Lightfoot is in discussion with a these ambulance services to support redesign of their clinical model to improve patient outcomes 8) Lightfoot continue to provide benchmarking data form HES to the Association of Ambulance Chief Executives Association of Ambulance Chief Executives (AACE) that has allowed the membership of English ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance to inform national policy. • Analysis of patient data with ambulance trusts identified regions across the country where ambulance trusts delivering an enhanced clinical model, which allowed increased rates of See, and Treat therefore significantly reducing the number of “avoidable attendances” to A&E departments of patients transported by ambulance. In one region (re point 8) this established better outcomes for patients but also significant financial savings to the health economy when patients were treated at scene rather than be transported to hospital. Using HEs data this way allows ACCE to share evidence for best practice. This also helped commissioners to appropriately fund this level of service provided by the ambulance trust. • Providing benchmarking data to the AACE has allowed the member ambulance trusts to explore the variation in outcomes for patients transported to hospital by ambulance. The on-going benefit is that the trusts will be able to highlighted opportunities for knowledge share and the transfer of best practice to improve patient outcomes in regions that had the greatest variation. • Association of Ambulance Chief Executives (AACE): provision of nationwide benchmarking solution to ACCE and ten national ambulance trusts utilising HES data. Supporting strategic objectives in their National Programme. Completing evidence based research to support national commissioning discussions.
LONDON NORTH WEST HEALTHCARE NHS TRUST	LONDON NORTH WEST HEALTHCARE NHS TRUST	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	Investigation of genetic and environmental factors underlying cardiovascular disease – the London Life Sciences Population (LOLIPOP) Study. The primary aims of the LOLIPOP study are to identify the genetic and environmental factors underlying the two fold increased risk of cardiovascular disease amongst UK Indian Asians compared with European whites. Data access is restricted to those named in section 7 of this agreement. Any changes will be notified to the NHS IC. The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.	No contact will be made with any individual(s) that could be identified from the information supplied, except as specified in the protocol and associated letters agreed between the Ealing Hospital NHS Trust, Uxbridge Road, Middx UB1 3HW and the NHS IC. Use of these Datasets are for the sole purpose set out above. The Data must not be shared with any other organisation or named individual not explicitly referred to within this agreement. If the information referred to herein is subject to an FOI or other request to share the Data, then agreement from the NHS IC must be sought before undertaking this. The Dataset must not be shared with any third party in the format in which it is provided to you by the NHS IC. Information tools derived from this Dataset will not be provided to any organisations without the specific consent of the NHS IC. Any publications derived from this Data by any party must be subject to ONS confidentiality guidance on the release of Health Statistics: http://www.ons.gov.uk/about/consultations/closed-consultations/disclosure-review-for-health-statistics---consultation-on-guidance/	The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.	The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.
LONDON NORTH WEST HEALTHCARE NHS TRUST	LONDON NORTH WEST HEALTHCARE NHS TRUST	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	Investigation of genetic and environmental factors underlying cardiovascular disease – the London Life Sciences Population (LOLIPOP) Study. The primary aims of the LOLIPOP study are to identify the genetic and environmental factors underlying the two fold increased risk of cardiovascular disease amongst UK Indian Asians compared with European whites. Data access is restricted to those named in section 7 of this agreement. Any changes will be notified to the NHS IC. The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.	No contact will be made with any individual(s) that could be identified from the information supplied, except as specified in the protocol and associated letters agreed between the Ealing Hospital NHS Trust, Uxbridge Road, Middx UB1 3HW and the NHS IC. Use of these Datasets are for the sole purpose set out above. The Data must not be shared with any other organisation or named individual not explicitly referred to within this agreement. If the information referred to herein is subject to an FOI or other request to share the Data, then agreement from the NHS IC must be sought before undertaking this. The Dataset must not be shared with any third party in the format in which it is provided to you by the NHS IC. Information tools derived from this Dataset will not be provided to any organisations without the specific consent of the NHS IC. Any publications derived from this Data by any party must be subject to ONS confidentiality guidance on the release of Health Statistics: http://www.ons.gov.uk/about/consultations/closed-consultations/disclosure-review-for-health-statistics---consultation-on-guidance/	The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.	The LOLIPOP study is a prospective population cohort comprising ~18,000 Indian Asian and ~12,000 European white men and women living in West London. Consenting subjects complete a questionnaire (recording current, past and family medical history, cardiovascular risk factors, current medications, alcohol and cigarette consumption) have measurements of height, weight, waist hip ratio and blood pressure, as well as a 12 lead ECG. Fasting blood samples are collected for routing haematology and biochemistry (including blood lipids). Aliqouts of plasma, serum and DNA are stored for future analyses. Baseline assessments are complete. We now plan to undertake follow-up to identify incident cardiovascular events, using death certification, hospital discharge coding and local cardiac databases. We ask the Information Centre to flag research participants, and inform LOLIPOP investigators of participants who have died along with the cause of death. Flagging should continue for 20+ years. Possible cardiovascular events, including deaths, will be verified against source data where possible. This will be done through review of hospital, primary care and coroners records.
LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE	LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE	MRIS - Flagging Current Status Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The need to research for potential genetic damage amongst British nuclear test veterans and the possibility of transmitted genetic alterations in their children has been a cornerstone requirement identified by members of the nuclear community for many years. A recent award from the Aged Veteran Fund (AVF) is now enabling such investigations to take place in a study led by Brunel University London in collaboration with the London School of Hygiene & Tropical Medicine. The project is part of a larger portfolio coordinated by the Nuclear Community Charity Fund (NCCF) on behalf of British Nuclear Test Veteran Association. This project will carry out chromosomal analysis of cells from nuclear test veterans and their children. The study will recruit 50 veteran family trios (father, mother, child) to provide samples for fluorescence in situ hybridisation (FISH)-based analyses to ask if there is any evidence of altered frequencies of chromosomal aberrations in veterans and/or their children when compared to 50 control family groups. Participants will be selected from a defined group of veterans known to have been present at nuclear tests and inclusion will not be related to ill-health (case veterans). The control group of veterans will be matched on age, service, rank and will have served at the same time in tropical regions but will be verified as not being present at test sites. All veterans will be interviewed for their medical and service history. Knowledge gained from this project will make significant in-roads into clarifying ongoing uncertainties about the possible impact on health by providing cytogenetic evidence to address identified issues and/or to dispel unfounded concerns. Outputs from this work will benefit the broader nuclear community by providing a scientific rationale that will improve understanding and, if genetic effects are observed, to inform health and social care providers to better support this community’s needs. The applicant has received approval for a “Consent for Consent” application from the Confidentiality Advisory Group (CAG) for the London School of Hygiene & Tropical Medicine (LSHTM) to receive NHS number, date of birth (for validation) and GP details of armed services veterans identified from Public Health England’s (PHE’s) nuclear test veteran’s cohort study via NHS Digital. LSHTM has gained Section 251 Approval to cover NHS Digital receiving and processing NHS Number and date of birth from PHE, and the receipt from NHS Digital of NHS number, date of birth and GP name and address code for the purpose of patient identification and recruitment via the GP practice. The applicant is applying to NHS Digital with S251 support for the contact data. PHE will supply NHS number and date of birth for selected cases (those who have been exposed to nuclear testing) and control veterans (those who have not been exposed) to NHS Digital for linkage to GP practice details. NHS Digital will remove deceased veterans and those with a diagnosis of cancer (other than non-melanoma skin cancer) from the list prior to sending on NHS number, date of birth, GP practice details and case/control status to the London School of Hygiene & Tropical Medicine for the purpose of patient identification and recruitment via GP practices. At this stage, only the above linkage (NHS number, date of birth, GP details and case/control status) from NHS Digital for the purpose of patient identification and recruitment via GP practices is being requested. However, the Section 251 application includes approval for all participants to be flagged for lifelong followup through national registers for death and cancer registration and hospital admissions via HES, subject to further funding for this aspect of the study. It is therefore anticipated that a subsequent amendment to NHS Digital for this additional work will be made in the future.	PHE will provide to LSHTM a completely pseudonymised data set containing the exposure data held on the Nuclear Test Veterans in the cohort. This will not include any NHS Digital data. These data will be used to select the most heavily exposed test veterans (using stratified random sampling based on number and location of tests attended, measured or estimated exposures, job role at test site) including personnel who flew through the dust cloud after each test. Unexposed controls will be matched on age, rank, service (RAF, Royal Navy, Army) and period of service in tropical regions. PHE will submit lists of identified patients to NHS Digital who hold the flagging data on these individuals. Participants who have died or have cancer registrations (other than non-melanoma skin cancer) will be excluded by NHS Digital. NHS Digital will also match the list against the national register to exclude the most recently deceased or cancer registered participants prior to linking with GP data and sending the finalised list [containing NHS number (for identification), DOB (for validation) and GP name and address] to LSHTM. This is the minimum identifiable data required in order to seek consent. Access to the data will only be by substantive employees of LSHTM. The LSHTM study team, who are substantive employees of LSHTM, will invite potential subjects to participate in the study via their GP practice. The LSHTM study team will write to each potential participant's GP, supplying the subject's NHS number and date of birth for identification, and explaining the purpose of the study. If the GP does not feel it is inappropriate for any reason, the GP practice is requested to pass on a letter, patient information sheet & consent form and reply slip with pre-paid addressed envelopes outlining the study and asking the couple to let the LSHTM study team know whether or not they might be willing to take part. Those replying and supplying a contact telephone number will be telephoned by the LSHTM study team to discuss the study, answering any questions the respondents may have. Fully informed written consent to participate in the study will then be obtained from willing eligible participants. Identifiable data is required in order to identify, contact, and gain consent from individuals to participate in the study (via their GP practice). This by definition would not be possible with anonymised or pseudonymised data. Sampling from the PHE nuclear test veteran’s cohort is a non-biased method of identifying potential participants. This is also an unbiased source of exposure data which will be used to identify those veterans who are likely to be at the highest risk of radiation exposure. The cohort also contains an unexposed set of veterans from which controls for this study will be selected. If the applicant were not able to utilise this cohort as the sampling frame, the study would have to rely on a biased set of volunteers. The response rate for veteran trios recruited is anticipated to be quite low due to the multi-step nature of the recruitment process. Initially, case veterans will be anonymously selected from the PHE cohort based on exposure history, with control veterans being selected to match on various variables (age, rank, service type and period of service). Once veterans are selected, NHS Digital will remove members who are deceased or registered with cancer (other than non-melanoma skin cancer) prior to sending to the study team. GP practices will then be contacted to confirm eligibility, at which point ineligible veterans will be excluded. Subsequently, eligible veterans and their wives or partners will both have to agree to take part in the study and they need to have had a biological child together, who is still alive and resident in the UK. Thus potentially eligible veterans will be lost at this stage of the recruitment process. Eligible and willing veteran couples will then forward a letter on behalf of the study team to their child, who must also be confirmed as eligible (via their GP) and agree to take part in the study before the veteran trio can be counted as one unit towards the overall response rate. Further to this, control veterans must be frequency matched on four different variables to case veterans (age, rank, service type and period of service), without knowing a priori which case veterans will respond and take part in the study. This will require four times as many controls to be selected a priori. Previous studies in this age-range have achieved a response rate of approximately 10% for individual respondents. It may therefore be prudent to assume that approximately 3-4% of case veterans will eventually participate as a complete veteran trio in this study, with approximately 1% of control veterans participating as a family trio once matching has been done. To avoid time-consuming and costly multiple applications to NHS Digital for recruitment purposes, the applicant is therefore seeking to receive NHS number, date of birth and GP practice details from 1500 case veterans (to allow for an eventual response rate of 3-4% for case trios), plus 6000 controls (to allow for a priori frequency matching on four variables). Appropriate controls will be in place to ensure that access to confidential research information is restricted only to those who need it. The study data will be electronically stored inside a secure network at LSHTM, apart from the percentage which are paper copies of study materials (reply slips, consent forms and telephone interview data). These will be stored in locked filing cabinets at LSHTM, accessible only to the direct study team. Data containing personal identifiers will be transferred to and from NHS Digital using their secure data transfer portal. This data will be stored on the secure server at LSHTM which can be accessed only by the LSHTM study team using their unique network password. Brunel University London staff will not have access to any NHS Digital (or otherwise) patient identifiable data as part of this application. Once consent has been gained and that data collected, the two organisations will liaise over patients using study ID (though again Brunel will not be accessing any NHS Digital data).	There will be no outputs produced directly from the data requested under this application. The wider project, which will be subject to a further application for data will produce a number of outputs and on completion of the study report, the main findings will be summarised and sent in a letter to those who participated in the study. The study results will also be published in peer-reviewed scientific journals and presented at conferences. Examples of conferences that this work may be presented at include the International Congress of Radiation Research 2019 in Manchester (the largest Congress for radiation research, held every 4 years); the Association for Radiation Research; the European Association for Radiation Research; the Radiation Protection Week (large EU supported conference); the Annual General Meetings of British Nuclear Test Veterans Association). The study is being conducted on behalf of the Nuclear Community Charity Fund, so there will also be presentations at nuclear community events and articles pertaining to the study results will be published in nuclear community publications. All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide.	The benefit of this application is patient identification and recruitment via GP practices to gain consent from individuals to participate in the study and not contacting deceased patients and causing distress to their families. Outcomes from this project will bring benefit to families in the nuclear community by providing them with the first ever comprehensive cytogenetic exploration to examine for possible genetic differences between members of their community and control family groups. The findings will underpin evidence-based information and education that will seek to reduce the reported confusion, anxiety and uncertainties voiced by members of the nuclear community. The findings, which will be translated for the benefit of the nuclear community, may also lead to further research with the aim of informing care & wellbeing programmes. Participating families will also benefit from a sense of contributing and being part of a research study, designed in partnership with the BNTVA that has the sole aim of seeking answers to outstanding questions within their community.
LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE	LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE	MRIS - List Cleaning Report	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The objective of the request is to receive information about fact of death and current postcode for members of the cohort prior to writing out therefore preventing the writing out to any people in the cohort who may have passed away and not writing to people who have moved address. The output is to create a 'live' consented database and the benefit is not to cause distress PhD Research Study on the Use of PROMs in Emergency Admissions, a feasibility study conducted to explore the use of Patient reported outcome measures in emergency admissions patient cohorts. The aim of the work is to assess the feasibility of retrospective PROMs in emergency admissions and follow-up PROMs to determine change in Health Related Quality of Life (HRQL) of patients following emergency hospital care. This is the last phase of a three year study and links directly with the National Emergency Laparotomy Audit (NELA), who if the study proves to be a success, will be looking at the feasibility of including PROMs in National Clinical Audit. In England, emergency admissions accounted for nearly 38% of all hospital admissions in 2012-13, with an increase of 47% over the last 15 years. Two thirds of hospital beds are occupied by people admitted as emergencies and the cost is approximately £12.5 billion. Of these, emergency general surgery represents approximately half of the general surgical workload and accounts for over 600,000 hospital admissions at a cost of £88 million a year. Furthermore, emergency admissions is an area whereby variation in clinical outcome is greater than for elective care, and an area where there is no information on patient-reported outcomes. Capturing PROMs for unexpected emergency admissions is primarily to measure the quality of health services. The aim of healthcare is to restore patients’ health to their full potential and health related quality of life (HRQL). Patients’ recovery can be compared with their pre-event baseline quality of life to determine the effectiveness of the health service. This, however, is impossible to obtain prior to an acute event and therefore collection of a retrospective PROM by patient recall to replace the baseline HRQL is an option. Patient Reported Outcomes Measures (PROMs) have the potential to transform healthcare delivery through enhancing patient-centred care, assessing relative clinical quality, comparing providers’ performance and evaluating the effectiveness of treatments. The development of routinely collected PROMs data in four elective surgical procedures in England has been heralded as one of the missing components of quality in the jigsaw in the evaluation of our health service. (DoH Guide to PROMs Methodology 2009, Black 2013). With growing acceptance of the importance of patients' views of their outcome, as well as clinicians' measures such as mortality and impairment, when evaluating interventions and assessing the quality of services, it is necessary to devise ways in which accurate PROMs can be obtained, since these provide information on the effectiveness of treatment, an important component in determining the quality of healthcare. Development to widen use of PROMs helps to focus the health service towards patient-centred care (Greenhalgh and Long 2004). There is sustained clinical and political interest in the systematic development of PROMS (Morris et al 2007, DoH NHS Outcomes Framework 2015-16, DoH The Mandate to the NHS 2015-16). This is a new study, a new phase as part of a 3 year doctoral programme of research on the topic of Patient Reported Outcomes Measures. This study applied for HRA approval in Nov 2016, and received approval in Dec 2016. Planned data collection to begin in March 2017. Study participant's (patient) death status (fact of death) from MRIS/ PDS is required to ensure that the researchers do not send follow-up PROMs questionnaires to patients who have passed away prior to follow-up. Emergency conditions account for 40% of NHS hospital admissions and have been an area of increasing resource use and political importance (DoH NHS Outcomes Framework 2015-16). There is mounting interest to extend the use of PROMs to emergency admissions. Participating trusts, national clinical audit group (NELA) will be informed of the research findings through dissemination through partnering providers and Collaborations for Leadership in Applied Health Research and Care (CLARHC) research network, at conferences and workshops- HSR symposium 2018, National PROMS conference 2018 and also though reports to national clinical audit collaborators as well as traditional forms of research dissemination pathways including academic conferences, publications in peer reviewed journals- e.g. BMJ journal for quality and safety, and a PhD thesis.	London School of Hygiene and Tropical Medicine (LSHTM) will securely transfer patient identifiers (NHS number, DOB, postcode) and unique study ID to NHS digital from consented patients of the study (patient recruitment is over 3 months from hospitals), the information will be requested monthly over 3 months before follow-up questionnaires are sent directly to patients. NHS Digital data for patients who do not respond to follow-up questionnaire will be deleted. NHS Digital will send any known fact of death with Unique study ID (and no other identifiers) from study participants to LSHTM. LSTHM stores the data on a secure server in LSHTM which can be only accessed by applicant and the study team at LSHTM. Data will only be accessed by individuals within the study team who have authorisation from the applicant (chief investigator of the study) to access the data for the purpose(s) described. Economic and Social Research Council PhD funding awards are provided through doctoral training centres for which LSHTM belong to the Bloomsbury Doctoral Training Centre partnerships which is managed by the University College London Institute Of Education, therefore the grant for the study is managed by Bloomsbury doctoral training Centre partnerships and this organisation also issues the funding letter. The PhD student conducting this study is a full time PhD student at the LSHTM and this study solely belongs to LSHTM's clinical governance; this includes the sponsorship and indemnity, quality and responsibility of the overall study. University College London Institute Of Education have no further involvement in this study. Knowledge of the fact of death of any study participants within this cohort study allows researchers to minimize any potential distress caused by sending a follow-up questionnaire to a participant who have passed away. Due to the nature of emergency admissions, there could be a 2-5% mortality within the first 2-3 months of hospital discharge in the study patient cohort.	All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. The following outputs will be produced : The main findings will be included in PhD thesis due in May 2018. The final report of results will be submitted to NELA in Dec 2017. This will cover all findings of the study including: factors influencing planning and implementation and key findings. Once finalised, this will be submitted for publication in the open access, peer-reviewed journals with an estimated submission date of January 2018. Further academic paper(s) will be published in open-access and peer-reviewed journal such as BMJ quality and safety on [methodology; cost and effectiveness of the feasilibty of using PROMs in emergency admissions]; impact on policy and clinical care. A simplified version of the findings will be disseminated to patient groups of interest (e.g. CLAHRC network, participating hospitals patient forums). Findings will be presented at conferences and events with CLARHC patient groups.	Request of data for FACT OF DEATH requirement only. The purpose is to enable the researcher not to cause distress. So the objective is not to write to any dead people, the output is to create a 'live' consented database and the benefit is not to cause distress. Study findings will be disseminated to participating providers and National Emergency Laparotomy Audit and at conferences such as HSR symposium 2018, National PROMS conference 2018 involving provider trusts and clinicians, reports to National clinical audit collaborators .They will be able to use patient reported outcomes for service improvement decisions and on-going benchmarking of services. There is prior engagement with the National Clinical Audit groups who are interested in adopting the use of Patient Reported Outcomes to clinical audit. There is significant interest from policy makers in extending the use of PROMs in emergency admissions. PROMs helps providers, commissioners to measure effectiveness and quality of care, extending the use of PROMs in emergency admissions allows for increased patient-centred care. Benefiting care users through understanding quality of care and quality of life post-treatment/care from the patients' perspective. Emergency Admissions make up nearly 40% of all hospital admissions in England and is increasing each year.
MCKINSEY & COMPANY	MCKINSEY & COMPANY	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, NHS England, NHS Improvement and Public Health England commission McKinsey & Company, Inc. United Kingdom (referred to as “McKinsey” hereafter) to work on projects which are procured by the NHS organisation within and outside of specific procurement framework agreements. The scope of this work is developed by the client organisation and covers a broad range as specified by the client including strategy, performance transformation, and organisational development. Examples are listed in the “specific outputs” section. McKinsey use HES data in order to provide fact-based answers to McKinsey’s NHS clients questions regarding identification, assessment and quantification of opportunities to improve the quality and efficiency of the NHS services that they deliver, or are responsible for overseeing and regulating. McKinsey have applied for a license renewal for HES data from 2013/14 to the present (ongoing quarterly managed service subscription, with a rolling retention of 3 full years plus latest available) in order to be able to look at trends in performance, expenditure, utilisation and demand. The specific purposes and types of analysis that McKinsey perform are the following: (1) Benchmarking and analysis of operational performance (2) Benchmarking and analysis of variation in utilisation rates and tariff spending (3) Analysis of historic trends in rates of activity and spending (4) Analysis of the impact of different service configuration options HES data will only be used in the context of services by McKinsey in England and will not be used for non-NHS (or social care) organisations or for organisations outside of England. McKinsey are requesting to maintain access to three years of historical data in order to monitor trends in performance, expenditure, utilisation and demand. Access to three years of data allows for the analysis of trends to identify cyclical patterns in utilisation as well as directional trends in performance, while also allowing for the identification of anomalies in activity. Furthermore this permits the measurement of the effectiveness of performance and cost improvement initiatives such as in tracking activity and expenditure following implementation of a cost improvement plan or QIPP initiative.	Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries. These are analysed further in Excel and Tableau. McKinsey currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets and in Tableau software. (1) Benchmarking and analysis of operational performance McKinsey have a standardised tool which is created annually using HES data. This tool is a "Hospital Diagnostic" which compares all NHS acute Trusts on a range of operational performance metrics (including case-mix adjusted average length of stay, day case and day of surgery admission rates by setting and specialty; proportion of A&E attendances resulting in admission by length of stay of that admission etc) against a peer group (tailored to each individual Trust). This analytical tool is created in Tableau. McKinsey also conduct ad hoc analyses for the same measures to look in more detail at performance, for example at site level, or for specific types of patients (e.g. sub-groups defined by age, gender and diagnosis cluster). Ad hoc analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (2) Benchmarking and analysis of variation in utilisation rates and tariff spending McKinsey have standardised approaches to measure variation in utilisation rates (by setting, patient type or demographic sub-group, specialty and different activity clusters) and associated tariff expenditure both within (at GP practice level) and between CCG commissioner peer groups (defined using ONS cluster groupings). Utilisation is measured as an activity rate (or associated tariff value) per 1,000 age-needs weighted population (or most appropriate population measure) and compared to CCG (or GP practice) peer group median, quartiles and deciles. This analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (3) Analysis of historic trends in rates of activity and spending. Operational performance and utilisation rates are measured over time at different frequencies, including yearly, monthly and weekly, in order to understand cyclical patterns and directional performance trends. This analysis is conducted in excel or Tableau using subsets of data extracted using standardised data queries from SAS. (4) Analysis of the impact of different service configuration options HES data is used to develop best estimates of baseline activity and capacity (defined as bed days for admitted patient care) for commissioners and providers, aggregated at service line level (defined by specialty and point of delivery). This is then forecasted forward using a range of sources of insight, data and triangulation methods (including, but not limited to, local and national historic trends described above), to develop growth assumptions and scenarios. A simulation is created, in excel or Tableau, to analyse how these baseline levels would change over time if service configuration changed.	It is not possible to provide full details of all specific outputs and timings because McKinsey work on multiple projects for a large number of different national, regional and local organisations across the NHS, including providers, commissioners and regulators. Some examples of outputs expected are set out below. During the course of the projects that McKinsey do with NHS organisations, McKinsey test the data and analysis with McKinsey’s clients, and where necessary update and replace the data with summary data provided by the clients. This is the case with commissioners and providers, but is not always possible due to limitations in analytical capabilities, resources, and their own access to data. Data is only shared with clients, and only in aggregated, non-patient identifiable formats with small numbers suppressed in line with the HES Analysis Guide’. McKinsey shares outputs in the following ways with clients: • McKinsey include aggregated, non-patient identifiable data in line with the small numbers guidance into Excel and Tableau models which McKinsey hand over to the NHS client • McKinsey publish graphs based on the aggregated, non-patient identifiable results of quantitative analysis in line with the small numbers guidance in reports given to McKinsey’s NHS clients • McKinsey present the aggregated, non-patient identifiable results in line with the small numbers guidance at meetings with NHS client stakeholders McKinsey do not directly publish the outputs in any journal articles or other public documents (e.g., white papers) nor do McKinsey directly present any data outputs in the public domain. McKinsey will only share aggregated analysis with its NHS clients in Excel, Tableau or PowerPoint charts, in full compliance with the small numbers guidance in the HES Analysis Guide. Current projects with NHS clients requiring access to HES include: • Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016. (Complete November 2016) • Ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations, due to end in November 2016. (Complete November 2016) • Current 2 month provision of financial recovery, improvement and sustainability to two STP footprints until mid November 2016. This work entails benchmarking providers across an entire health system, predicting patient flow through different reconfiguration models, and understanding key health activity metrics of their populations. We are in discussions with other STP footprints to commence similar support with their implementation programmes. (Complete November 2016) • McKinsey expect to begin work in the winter 2017 with a large acute teaching trust in London to review productivity opportunities within specific service lines. Project start date is still under discussion. • McKinsey have responded to a tender with an NHS region to evaluate their urgent and emergency vanguard programmes. The project would run from January to April 2017. Access to HES would enable analysis to understand historic patient flows for emergency care (A&E and inpatient) to compare with in-year hospital data from NHS trusts participating in the vanguard. The data or outputs will not be used (directly or indirectly) for sales or marketing purposes by McKinsey & Company Inc. United Kingdom or by any other non-NHS organisation and can only be used for the purposes of the promotion of health.	Benefits achieved to date are : 1. McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Deep dives on case mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. McKinsey completed a 13 week project in December 2015 working with a group of CCGs in London and their stakeholder trusts to review productivity savings over the next five years to achieve their planned system transformation. The project ran across three phases to identify and then develop proof of concept projects to achieve these savings. HES data was used to benchmark productivity opportunities in operational performance by comparing case mix adjusted length of stay across the four stakeholder trusts and with their comparator trusts across England. In the first phase, the McKinsey team identified over £500m of potential productivity improvements, drawing on both the HES analysis as well as detailed benchmarking of the trusts’ financial accounts. In the second phase, McKinsey worked with stakeholders to develop ‘proof of concept’ projects across elective orthopaedics, end of life care, and bank and agency staff costs. For each of these categories of spend, the team undertook a productivity diagnosis and developed a joint implementation plan based on the findings. HES data was used to measure historic variation in elective orthopaedics around case-mix adjusted length of stay, procedures per pseudonymised consultant, and other activity metrics. The McKinsey team used this benchmarking analysis, presented in aggregated PowerPoint tables to convene working groups of clinicians and non-clinicians involved in elective orthopaedic care to agree upon a best practice pathway to reduce unwarranted variation. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. McKinsey completed a 3 month study in March 2016 to develop a single hospital service across a financially challenged health economy in the North of England currently served by three large trusts. The region was facing high levels of health inequalities and poor outcomes. The purpose of the project was to improve clinical care through the reduction of variation in quality, outcomes, patient experience and cost through the consolidation of independent services into a single service. HES data was critical for identifying addressable variations in length of stay, volumes and productivity across the hospitals and through benchmarking with peer trusts. Data was presented in aggregated tables comparing activity across the hospital sites, alongside data on clinical outcomes from the national clinical audits. These were used to align stakeholders around the value of the single hospital service. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 4. McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10 week delivery phase, the consortium supported hospital divisions to develop a strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. HES benchmarking against comparator peers was used to assess and strengthen the plans. The team’s work helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total in-year savings of £79m. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 5.McKinsey and Company has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. After successful pilot implementation, impact was validated using actual observed pilot data, confirming operational formats and leading to the roll-out of the operating model across the whole of the ambulance service. The project is set to continue in 2017. 6. McKinsey and Company completed a 14 week project up to December 2016 to provide financial recovery, improvement and sustainability support to an STP footprint in the East of England. The health region was facing a projected deficit of close to £500m by 2021 and is one of the most financially challenged health economies in the country. The project team used HES data to drive operational performance benchmarking of the CCG’s historic performance against comparator CCGs, and to compare internal activity trends, such as rates of A&E attendances and outpatient attendances by GP practice. The team used these benchmarks to assess the size of the total opportunity to improve, evaluate the ambition of current QIPP schemes, identify new opportunities for efficiency savings, and to support development of detailed delivery plans to implement these schemes and address remaining financial gap. 7. At the end of 2016, McKinsey and Company completed an 18 month strategic partnership with a London CCG. The objectives of the work were to ensure the organisational priorities were correct given the changing landscape, local health needs and the quality and performance of local services, as a basis for organisational work to ensure the organisation had the capacity and capability to deliver these priorities. HES data was used at the outset of the programme to conduct a broad and detailed diagnostic of historic 3 year trends in activity across acute and emergency care, and paediatric and maternity care to understand activity growth and service needs across the borough; this included analysis such as identifying potentially avoidable hospitalisations and A&E visits by age group. This diagnostic phase helped facilitate a thorough review of the priorities and draft strategic plans based upon the comprehensive analysis of service utilisation patterns. This work led to the development of 5 year commissioning strategic priorities that address the CCG’s greatest health challenges, and then informed a broader organisational development programme, which included an organisational review of the CCG’s commissioning functions and structure Expected future benefits for individual projects vary, but in almost all cases involve identification and quantification of opportunities to improve the quality of patient care and population health, and to deliver more effective, efficient care. Target dates (for expected improvements) also vary but in almost all cases are within 3 years and often include within year opportunities for service improvements and/or savings. Examples of the benefits expected for the projects described above are: Examples of the benefits expected for the seven projects described above: 1. The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 4. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 5. The ambulance service has adopted a new operational model developed following a diagnostic of deteriorating operational performance informed by benchmarking using HES A&E data. It is expected that the ambulance service will roll-out the necessary operational changes over the coming months, resulting in a significantly higher productivity of current staff and more consistent achievement of the nationally mandated access targets. 6. The outputs of the overall STP project, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of >200 STP wide initiatives, include an expected savings of ~£40m at the end of the fiscal year. 7. It is expected that this CCG’s 5 year plan, informed by the HES-derived benchmarks, and the improved capacity and capabilities within the CCG will lead to better allocative efficiency of resources, which should in turn improve access to quality care tailored to the population’s needs.
MCKINSEY & COMPANY	MCKINSEY & COMPANY	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, NHS England, NHS Improvement and Public Health England commission McKinsey & Company, Inc. United Kingdom (referred to as “McKinsey” hereafter) to work on projects which are procured by the NHS organisation within and outside of specific procurement framework agreements. The scope of this work is developed by the client organisation and covers a broad range as specified by the client including strategy, performance transformation, and organisational development. Examples are listed in the “specific outputs” section. McKinsey use HES data in order to provide fact-based answers to McKinsey’s NHS clients questions regarding identification, assessment and quantification of opportunities to improve the quality and efficiency of the NHS services that they deliver, or are responsible for overseeing and regulating. McKinsey have applied for a license renewal for HES data from 2013/14 to the present (ongoing quarterly managed service subscription, with a rolling retention of 3 full years plus latest available) in order to be able to look at trends in performance, expenditure, utilisation and demand. The specific purposes and types of analysis that McKinsey perform are the following: (1) Benchmarking and analysis of operational performance (2) Benchmarking and analysis of variation in utilisation rates and tariff spending (3) Analysis of historic trends in rates of activity and spending (4) Analysis of the impact of different service configuration options HES data will only be used in the context of services by McKinsey in England and will not be used for non-NHS (or social care) organisations or for organisations outside of England. McKinsey are requesting to maintain access to three years of historical data in order to monitor trends in performance, expenditure, utilisation and demand. Access to three years of data allows for the analysis of trends to identify cyclical patterns in utilisation as well as directional trends in performance, while also allowing for the identification of anomalies in activity. Furthermore this permits the measurement of the effectiveness of performance and cost improvement initiatives such as in tracking activity and expenditure following implementation of a cost improvement plan or QIPP initiative.	Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries. These are analysed further in Excel and Tableau. McKinsey currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets and in Tableau software. (1) Benchmarking and analysis of operational performance McKinsey have a standardised tool which is created annually using HES data. This tool is a "Hospital Diagnostic" which compares all NHS acute Trusts on a range of operational performance metrics (including case-mix adjusted average length of stay, day case and day of surgery admission rates by setting and specialty; proportion of A&E attendances resulting in admission by length of stay of that admission etc) against a peer group (tailored to each individual Trust). This analytical tool is created in Tableau. McKinsey also conduct ad hoc analyses for the same measures to look in more detail at performance, for example at site level, or for specific types of patients (e.g. sub-groups defined by age, gender and diagnosis cluster). Ad hoc analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (2) Benchmarking and analysis of variation in utilisation rates and tariff spending McKinsey have standardised approaches to measure variation in utilisation rates (by setting, patient type or demographic sub-group, specialty and different activity clusters) and associated tariff expenditure both within (at GP practice level) and between CCG commissioner peer groups (defined using ONS cluster groupings). Utilisation is measured as an activity rate (or associated tariff value) per 1,000 age-needs weighted population (or most appropriate population measure) and compared to CCG (or GP practice) peer group median, quartiles and deciles. This analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (3) Analysis of historic trends in rates of activity and spending. Operational performance and utilisation rates are measured over time at different frequencies, including yearly, monthly and weekly, in order to understand cyclical patterns and directional performance trends. This analysis is conducted in excel or Tableau using subsets of data extracted using standardised data queries from SAS. (4) Analysis of the impact of different service configuration options HES data is used to develop best estimates of baseline activity and capacity (defined as bed days for admitted patient care) for commissioners and providers, aggregated at service line level (defined by specialty and point of delivery). This is then forecasted forward using a range of sources of insight, data and triangulation methods (including, but not limited to, local and national historic trends described above), to develop growth assumptions and scenarios. A simulation is created, in excel or Tableau, to analyse how these baseline levels would change over time if service configuration changed.	It is not possible to provide full details of all specific outputs and timings because McKinsey work on multiple projects for a large number of different national, regional and local organisations across the NHS, including providers, commissioners and regulators. Some examples of outputs expected are set out below. During the course of the projects that McKinsey do with NHS organisations, McKinsey test the data and analysis with McKinsey’s clients, and where necessary update and replace the data with summary data provided by the clients. This is the case with commissioners and providers, but is not always possible due to limitations in analytical capabilities, resources, and their own access to data. Data is only shared with clients, and only in aggregated, non-patient identifiable formats with small numbers suppressed in line with the HES Analysis Guide’. McKinsey shares outputs in the following ways with clients: • McKinsey include aggregated, non-patient identifiable data in line with the small numbers guidance into Excel and Tableau models which McKinsey hand over to the NHS client • McKinsey publish graphs based on the aggregated, non-patient identifiable results of quantitative analysis in line with the small numbers guidance in reports given to McKinsey’s NHS clients • McKinsey present the aggregated, non-patient identifiable results in line with the small numbers guidance at meetings with NHS client stakeholders McKinsey do not directly publish the outputs in any journal articles or other public documents (e.g., white papers) nor do McKinsey directly present any data outputs in the public domain. McKinsey will only share aggregated analysis with its NHS clients in Excel, Tableau or PowerPoint charts, in full compliance with the small numbers guidance in the HES Analysis Guide. Current projects with NHS clients requiring access to HES include: • Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016. (Complete November 2016) • Ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations, due to end in November 2016. (Complete November 2016) • Current 2 month provision of financial recovery, improvement and sustainability to two STP footprints until mid November 2016. This work entails benchmarking providers across an entire health system, predicting patient flow through different reconfiguration models, and understanding key health activity metrics of their populations. We are in discussions with other STP footprints to commence similar support with their implementation programmes. (Complete November 2016) • McKinsey expect to begin work in the winter 2017 with a large acute teaching trust in London to review productivity opportunities within specific service lines. Project start date is still under discussion. • McKinsey have responded to a tender with an NHS region to evaluate their urgent and emergency vanguard programmes. The project would run from January to April 2017. Access to HES would enable analysis to understand historic patient flows for emergency care (A&E and inpatient) to compare with in-year hospital data from NHS trusts participating in the vanguard. The data or outputs will not be used (directly or indirectly) for sales or marketing purposes by McKinsey & Company Inc. United Kingdom or by any other non-NHS organisation and can only be used for the purposes of the promotion of health.	Benefits achieved to date are : 1. McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Deep dives on case mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. McKinsey completed a 13 week project in December 2015 working with a group of CCGs in London and their stakeholder trusts to review productivity savings over the next five years to achieve their planned system transformation. The project ran across three phases to identify and then develop proof of concept projects to achieve these savings. HES data was used to benchmark productivity opportunities in operational performance by comparing case mix adjusted length of stay across the four stakeholder trusts and with their comparator trusts across England. In the first phase, the McKinsey team identified over £500m of potential productivity improvements, drawing on both the HES analysis as well as detailed benchmarking of the trusts’ financial accounts. In the second phase, McKinsey worked with stakeholders to develop ‘proof of concept’ projects across elective orthopaedics, end of life care, and bank and agency staff costs. For each of these categories of spend, the team undertook a productivity diagnosis and developed a joint implementation plan based on the findings. HES data was used to measure historic variation in elective orthopaedics around case-mix adjusted length of stay, procedures per pseudonymised consultant, and other activity metrics. The McKinsey team used this benchmarking analysis, presented in aggregated PowerPoint tables to convene working groups of clinicians and non-clinicians involved in elective orthopaedic care to agree upon a best practice pathway to reduce unwarranted variation. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. McKinsey completed a 3 month study in March 2016 to develop a single hospital service across a financially challenged health economy in the North of England currently served by three large trusts. The region was facing high levels of health inequalities and poor outcomes. The purpose of the project was to improve clinical care through the reduction of variation in quality, outcomes, patient experience and cost through the consolidation of independent services into a single service. HES data was critical for identifying addressable variations in length of stay, volumes and productivity across the hospitals and through benchmarking with peer trusts. Data was presented in aggregated tables comparing activity across the hospital sites, alongside data on clinical outcomes from the national clinical audits. These were used to align stakeholders around the value of the single hospital service. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 4. McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10 week delivery phase, the consortium supported hospital divisions to develop a strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. HES benchmarking against comparator peers was used to assess and strengthen the plans. The team’s work helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total in-year savings of £79m. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 5.McKinsey and Company has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. After successful pilot implementation, impact was validated using actual observed pilot data, confirming operational formats and leading to the roll-out of the operating model across the whole of the ambulance service. The project is set to continue in 2017. 6. McKinsey and Company completed a 14 week project up to December 2016 to provide financial recovery, improvement and sustainability support to an STP footprint in the East of England. The health region was facing a projected deficit of close to £500m by 2021 and is one of the most financially challenged health economies in the country. The project team used HES data to drive operational performance benchmarking of the CCG’s historic performance against comparator CCGs, and to compare internal activity trends, such as rates of A&E attendances and outpatient attendances by GP practice. The team used these benchmarks to assess the size of the total opportunity to improve, evaluate the ambition of current QIPP schemes, identify new opportunities for efficiency savings, and to support development of detailed delivery plans to implement these schemes and address remaining financial gap. 7. At the end of 2016, McKinsey and Company completed an 18 month strategic partnership with a London CCG. The objectives of the work were to ensure the organisational priorities were correct given the changing landscape, local health needs and the quality and performance of local services, as a basis for organisational work to ensure the organisation had the capacity and capability to deliver these priorities. HES data was used at the outset of the programme to conduct a broad and detailed diagnostic of historic 3 year trends in activity across acute and emergency care, and paediatric and maternity care to understand activity growth and service needs across the borough; this included analysis such as identifying potentially avoidable hospitalisations and A&E visits by age group. This diagnostic phase helped facilitate a thorough review of the priorities and draft strategic plans based upon the comprehensive analysis of service utilisation patterns. This work led to the development of 5 year commissioning strategic priorities that address the CCG’s greatest health challenges, and then informed a broader organisational development programme, which included an organisational review of the CCG’s commissioning functions and structure Expected future benefits for individual projects vary, but in almost all cases involve identification and quantification of opportunities to improve the quality of patient care and population health, and to deliver more effective, efficient care. Target dates (for expected improvements) also vary but in almost all cases are within 3 years and often include within year opportunities for service improvements and/or savings. Examples of the benefits expected for the projects described above are: Examples of the benefits expected for the seven projects described above: 1. The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 4. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 5. The ambulance service has adopted a new operational model developed following a diagnostic of deteriorating operational performance informed by benchmarking using HES A&E data. It is expected that the ambulance service will roll-out the necessary operational changes over the coming months, resulting in a significantly higher productivity of current staff and more consistent achievement of the nationally mandated access targets. 6. The outputs of the overall STP project, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of >200 STP wide initiatives, include an expected savings of ~£40m at the end of the fiscal year. 7. It is expected that this CCG’s 5 year plan, informed by the HES-derived benchmarks, and the improved capacity and capabilities within the CCG will lead to better allocative efficiency of resources, which should in turn improve access to quality care tailored to the population’s needs.
MCKINSEY & COMPANY	MCKINSEY & COMPANY	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, NHS England, NHS Improvement and Public Health England commission McKinsey & Company, Inc. United Kingdom (referred to as “McKinsey” hereafter) to work on projects which are procured by the NHS organisation within and outside of specific procurement framework agreements. The scope of this work is developed by the client organisation and covers a broad range as specified by the client including strategy, performance transformation, and organisational development. Examples are listed in the “specific outputs” section. McKinsey use HES data in order to provide fact-based answers to McKinsey’s NHS clients questions regarding identification, assessment and quantification of opportunities to improve the quality and efficiency of the NHS services that they deliver, or are responsible for overseeing and regulating. McKinsey have applied for a license renewal for HES data from 2013/14 to the present (ongoing quarterly managed service subscription, with a rolling retention of 3 full years plus latest available) in order to be able to look at trends in performance, expenditure, utilisation and demand. The specific purposes and types of analysis that McKinsey perform are the following: (1) Benchmarking and analysis of operational performance (2) Benchmarking and analysis of variation in utilisation rates and tariff spending (3) Analysis of historic trends in rates of activity and spending (4) Analysis of the impact of different service configuration options HES data will only be used in the context of services by McKinsey in England and will not be used for non-NHS (or social care) organisations or for organisations outside of England. McKinsey are requesting to maintain access to three years of historical data in order to monitor trends in performance, expenditure, utilisation and demand. Access to three years of data allows for the analysis of trends to identify cyclical patterns in utilisation as well as directional trends in performance, while also allowing for the identification of anomalies in activity. Furthermore this permits the measurement of the effectiveness of performance and cost improvement initiatives such as in tracking activity and expenditure following implementation of a cost improvement plan or QIPP initiative.	Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries. These are analysed further in Excel and Tableau. McKinsey currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets and in Tableau software. (1) Benchmarking and analysis of operational performance McKinsey have a standardised tool which is created annually using HES data. This tool is a "Hospital Diagnostic" which compares all NHS acute Trusts on a range of operational performance metrics (including case-mix adjusted average length of stay, day case and day of surgery admission rates by setting and specialty; proportion of A&E attendances resulting in admission by length of stay of that admission etc) against a peer group (tailored to each individual Trust). This analytical tool is created in Tableau. McKinsey also conduct ad hoc analyses for the same measures to look in more detail at performance, for example at site level, or for specific types of patients (e.g. sub-groups defined by age, gender and diagnosis cluster). Ad hoc analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (2) Benchmarking and analysis of variation in utilisation rates and tariff spending McKinsey have standardised approaches to measure variation in utilisation rates (by setting, patient type or demographic sub-group, specialty and different activity clusters) and associated tariff expenditure both within (at GP practice level) and between CCG commissioner peer groups (defined using ONS cluster groupings). Utilisation is measured as an activity rate (or associated tariff value) per 1,000 age-needs weighted population (or most appropriate population measure) and compared to CCG (or GP practice) peer group median, quartiles and deciles. This analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (3) Analysis of historic trends in rates of activity and spending. Operational performance and utilisation rates are measured over time at different frequencies, including yearly, monthly and weekly, in order to understand cyclical patterns and directional performance trends. This analysis is conducted in excel or Tableau using subsets of data extracted using standardised data queries from SAS. (4) Analysis of the impact of different service configuration options HES data is used to develop best estimates of baseline activity and capacity (defined as bed days for admitted patient care) for commissioners and providers, aggregated at service line level (defined by specialty and point of delivery). This is then forecasted forward using a range of sources of insight, data and triangulation methods (including, but not limited to, local and national historic trends described above), to develop growth assumptions and scenarios. A simulation is created, in excel or Tableau, to analyse how these baseline levels would change over time if service configuration changed.	It is not possible to provide full details of all specific outputs and timings because McKinsey work on multiple projects for a large number of different national, regional and local organisations across the NHS, including providers, commissioners and regulators. Some examples of outputs expected are set out below. During the course of the projects that McKinsey do with NHS organisations, McKinsey test the data and analysis with McKinsey’s clients, and where necessary update and replace the data with summary data provided by the clients. This is the case with commissioners and providers, but is not always possible due to limitations in analytical capabilities, resources, and their own access to data. Data is only shared with clients, and only in aggregated, non-patient identifiable formats with small numbers suppressed in line with the HES Analysis Guide’. McKinsey shares outputs in the following ways with clients: • McKinsey include aggregated, non-patient identifiable data in line with the small numbers guidance into Excel and Tableau models which McKinsey hand over to the NHS client • McKinsey publish graphs based on the aggregated, non-patient identifiable results of quantitative analysis in line with the small numbers guidance in reports given to McKinsey’s NHS clients • McKinsey present the aggregated, non-patient identifiable results in line with the small numbers guidance at meetings with NHS client stakeholders McKinsey do not directly publish the outputs in any journal articles or other public documents (e.g., white papers) nor do McKinsey directly present any data outputs in the public domain. McKinsey will only share aggregated analysis with its NHS clients in Excel, Tableau or PowerPoint charts, in full compliance with the small numbers guidance in the HES Analysis Guide. Current projects with NHS clients requiring access to HES include: • Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016. (Complete November 2016) • Ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations, due to end in November 2016. (Complete November 2016) • Current 2 month provision of financial recovery, improvement and sustainability to two STP footprints until mid November 2016. This work entails benchmarking providers across an entire health system, predicting patient flow through different reconfiguration models, and understanding key health activity metrics of their populations. We are in discussions with other STP footprints to commence similar support with their implementation programmes. (Complete November 2016) • McKinsey expect to begin work in the winter 2017 with a large acute teaching trust in London to review productivity opportunities within specific service lines. Project start date is still under discussion. • McKinsey have responded to a tender with an NHS region to evaluate their urgent and emergency vanguard programmes. The project would run from January to April 2017. Access to HES would enable analysis to understand historic patient flows for emergency care (A&E and inpatient) to compare with in-year hospital data from NHS trusts participating in the vanguard. The data or outputs will not be used (directly or indirectly) for sales or marketing purposes by McKinsey & Company Inc. United Kingdom or by any other non-NHS organisation and can only be used for the purposes of the promotion of health.	Benefits achieved to date are : 1. McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Deep dives on case mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. McKinsey completed a 13 week project in December 2015 working with a group of CCGs in London and their stakeholder trusts to review productivity savings over the next five years to achieve their planned system transformation. The project ran across three phases to identify and then develop proof of concept projects to achieve these savings. HES data was used to benchmark productivity opportunities in operational performance by comparing case mix adjusted length of stay across the four stakeholder trusts and with their comparator trusts across England. In the first phase, the McKinsey team identified over £500m of potential productivity improvements, drawing on both the HES analysis as well as detailed benchmarking of the trusts’ financial accounts. In the second phase, McKinsey worked with stakeholders to develop ‘proof of concept’ projects across elective orthopaedics, end of life care, and bank and agency staff costs. For each of these categories of spend, the team undertook a productivity diagnosis and developed a joint implementation plan based on the findings. HES data was used to measure historic variation in elective orthopaedics around case-mix adjusted length of stay, procedures per pseudonymised consultant, and other activity metrics. The McKinsey team used this benchmarking analysis, presented in aggregated PowerPoint tables to convene working groups of clinicians and non-clinicians involved in elective orthopaedic care to agree upon a best practice pathway to reduce unwarranted variation. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. McKinsey completed a 3 month study in March 2016 to develop a single hospital service across a financially challenged health economy in the North of England currently served by three large trusts. The region was facing high levels of health inequalities and poor outcomes. The purpose of the project was to improve clinical care through the reduction of variation in quality, outcomes, patient experience and cost through the consolidation of independent services into a single service. HES data was critical for identifying addressable variations in length of stay, volumes and productivity across the hospitals and through benchmarking with peer trusts. Data was presented in aggregated tables comparing activity across the hospital sites, alongside data on clinical outcomes from the national clinical audits. These were used to align stakeholders around the value of the single hospital service. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 4. McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10 week delivery phase, the consortium supported hospital divisions to develop a strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. HES benchmarking against comparator peers was used to assess and strengthen the plans. The team’s work helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total in-year savings of £79m. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 5.McKinsey and Company has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. After successful pilot implementation, impact was validated using actual observed pilot data, confirming operational formats and leading to the roll-out of the operating model across the whole of the ambulance service. The project is set to continue in 2017. 6. McKinsey and Company completed a 14 week project up to December 2016 to provide financial recovery, improvement and sustainability support to an STP footprint in the East of England. The health region was facing a projected deficit of close to £500m by 2021 and is one of the most financially challenged health economies in the country. The project team used HES data to drive operational performance benchmarking of the CCG’s historic performance against comparator CCGs, and to compare internal activity trends, such as rates of A&E attendances and outpatient attendances by GP practice. The team used these benchmarks to assess the size of the total opportunity to improve, evaluate the ambition of current QIPP schemes, identify new opportunities for efficiency savings, and to support development of detailed delivery plans to implement these schemes and address remaining financial gap. 7. At the end of 2016, McKinsey and Company completed an 18 month strategic partnership with a London CCG. The objectives of the work were to ensure the organisational priorities were correct given the changing landscape, local health needs and the quality and performance of local services, as a basis for organisational work to ensure the organisation had the capacity and capability to deliver these priorities. HES data was used at the outset of the programme to conduct a broad and detailed diagnostic of historic 3 year trends in activity across acute and emergency care, and paediatric and maternity care to understand activity growth and service needs across the borough; this included analysis such as identifying potentially avoidable hospitalisations and A&E visits by age group. This diagnostic phase helped facilitate a thorough review of the priorities and draft strategic plans based upon the comprehensive analysis of service utilisation patterns. This work led to the development of 5 year commissioning strategic priorities that address the CCG’s greatest health challenges, and then informed a broader organisational development programme, which included an organisational review of the CCG’s commissioning functions and structure Expected future benefits for individual projects vary, but in almost all cases involve identification and quantification of opportunities to improve the quality of patient care and population health, and to deliver more effective, efficient care. Target dates (for expected improvements) also vary but in almost all cases are within 3 years and often include within year opportunities for service improvements and/or savings. Examples of the benefits expected for the projects described above are: Examples of the benefits expected for the seven projects described above: 1. The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 4. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 5. The ambulance service has adopted a new operational model developed following a diagnostic of deteriorating operational performance informed by benchmarking using HES A&E data. It is expected that the ambulance service will roll-out the necessary operational changes over the coming months, resulting in a significantly higher productivity of current staff and more consistent achievement of the nationally mandated access targets. 6. The outputs of the overall STP project, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of >200 STP wide initiatives, include an expected savings of ~£40m at the end of the fiscal year. 7. It is expected that this CCG’s 5 year plan, informed by the HES-derived benchmarks, and the improved capacity and capabilities within the CCG will lead to better allocative efficiency of resources, which should in turn improve access to quality care tailored to the population’s needs.
MCKINSEY & COMPANY	MCKINSEY & COMPANY	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, NHS England, NHS Improvement and Public Health England commission McKinsey & Company, Inc. United Kingdom (referred to as “McKinsey” hereafter) to work on projects which are procured by the NHS organisation within and outside of specific procurement framework agreements. The scope of this work is developed by the client organisation and covers a broad range as specified by the client including strategy, performance transformation, and organisational development. Examples are listed in the “specific outputs” section. McKinsey use HES data in order to provide fact-based answers to McKinsey’s NHS clients questions regarding identification, assessment and quantification of opportunities to improve the quality and efficiency of the NHS services that they deliver, or are responsible for overseeing and regulating. McKinsey have applied for a license renewal for HES data from 2013/14 to the present (ongoing quarterly managed service subscription, with a rolling retention of 3 full years plus latest available) in order to be able to look at trends in performance, expenditure, utilisation and demand. The specific purposes and types of analysis that McKinsey perform are the following: (1) Benchmarking and analysis of operational performance (2) Benchmarking and analysis of variation in utilisation rates and tariff spending (3) Analysis of historic trends in rates of activity and spending (4) Analysis of the impact of different service configuration options HES data will only be used in the context of services by McKinsey in England and will not be used for non-NHS (or social care) organisations or for organisations outside of England. McKinsey are requesting to maintain access to three years of historical data in order to monitor trends in performance, expenditure, utilisation and demand. Access to three years of data allows for the analysis of trends to identify cyclical patterns in utilisation as well as directional trends in performance, while also allowing for the identification of anomalies in activity. Furthermore this permits the measurement of the effectiveness of performance and cost improvement initiatives such as in tracking activity and expenditure following implementation of a cost improvement plan or QIPP initiative.	Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries. These are analysed further in Excel and Tableau. McKinsey currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets and in Tableau software. (1) Benchmarking and analysis of operational performance McKinsey have a standardised tool which is created annually using HES data. This tool is a "Hospital Diagnostic" which compares all NHS acute Trusts on a range of operational performance metrics (including case-mix adjusted average length of stay, day case and day of surgery admission rates by setting and specialty; proportion of A&E attendances resulting in admission by length of stay of that admission etc) against a peer group (tailored to each individual Trust). This analytical tool is created in Tableau. McKinsey also conduct ad hoc analyses for the same measures to look in more detail at performance, for example at site level, or for specific types of patients (e.g. sub-groups defined by age, gender and diagnosis cluster). Ad hoc analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (2) Benchmarking and analysis of variation in utilisation rates and tariff spending McKinsey have standardised approaches to measure variation in utilisation rates (by setting, patient type or demographic sub-group, specialty and different activity clusters) and associated tariff expenditure both within (at GP practice level) and between CCG commissioner peer groups (defined using ONS cluster groupings). Utilisation is measured as an activity rate (or associated tariff value) per 1,000 age-needs weighted population (or most appropriate population measure) and compared to CCG (or GP practice) peer group median, quartiles and deciles. This analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database. (3) Analysis of historic trends in rates of activity and spending. Operational performance and utilisation rates are measured over time at different frequencies, including yearly, monthly and weekly, in order to understand cyclical patterns and directional performance trends. This analysis is conducted in excel or Tableau using subsets of data extracted using standardised data queries from SAS. (4) Analysis of the impact of different service configuration options HES data is used to develop best estimates of baseline activity and capacity (defined as bed days for admitted patient care) for commissioners and providers, aggregated at service line level (defined by specialty and point of delivery). This is then forecasted forward using a range of sources of insight, data and triangulation methods (including, but not limited to, local and national historic trends described above), to develop growth assumptions and scenarios. A simulation is created, in excel or Tableau, to analyse how these baseline levels would change over time if service configuration changed.	It is not possible to provide full details of all specific outputs and timings because McKinsey work on multiple projects for a large number of different national, regional and local organisations across the NHS, including providers, commissioners and regulators. Some examples of outputs expected are set out below. During the course of the projects that McKinsey do with NHS organisations, McKinsey test the data and analysis with McKinsey’s clients, and where necessary update and replace the data with summary data provided by the clients. This is the case with commissioners and providers, but is not always possible due to limitations in analytical capabilities, resources, and their own access to data. Data is only shared with clients, and only in aggregated, non-patient identifiable formats with small numbers suppressed in line with the HES Analysis Guide’. McKinsey shares outputs in the following ways with clients: • McKinsey include aggregated, non-patient identifiable data in line with the small numbers guidance into Excel and Tableau models which McKinsey hand over to the NHS client • McKinsey publish graphs based on the aggregated, non-patient identifiable results of quantitative analysis in line with the small numbers guidance in reports given to McKinsey’s NHS clients • McKinsey present the aggregated, non-patient identifiable results in line with the small numbers guidance at meetings with NHS client stakeholders McKinsey do not directly publish the outputs in any journal articles or other public documents (e.g., white papers) nor do McKinsey directly present any data outputs in the public domain. McKinsey will only share aggregated analysis with its NHS clients in Excel, Tableau or PowerPoint charts, in full compliance with the small numbers guidance in the HES Analysis Guide. Current projects with NHS clients requiring access to HES include: • Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016. (Complete November 2016) • Ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations, due to end in November 2016. (Complete November 2016) • Current 2 month provision of financial recovery, improvement and sustainability to two STP footprints until mid November 2016. This work entails benchmarking providers across an entire health system, predicting patient flow through different reconfiguration models, and understanding key health activity metrics of their populations. We are in discussions with other STP footprints to commence similar support with their implementation programmes. (Complete November 2016) • McKinsey expect to begin work in the winter 2017 with a large acute teaching trust in London to review productivity opportunities within specific service lines. Project start date is still under discussion. • McKinsey have responded to a tender with an NHS region to evaluate their urgent and emergency vanguard programmes. The project would run from January to April 2017. Access to HES would enable analysis to understand historic patient flows for emergency care (A&E and inpatient) to compare with in-year hospital data from NHS trusts participating in the vanguard. The data or outputs will not be used (directly or indirectly) for sales or marketing purposes by McKinsey & Company Inc. United Kingdom or by any other non-NHS organisation and can only be used for the purposes of the promotion of health.	Benefits achieved to date are : 1. McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Deep dives on case mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. McKinsey completed a 13 week project in December 2015 working with a group of CCGs in London and their stakeholder trusts to review productivity savings over the next five years to achieve their planned system transformation. The project ran across three phases to identify and then develop proof of concept projects to achieve these savings. HES data was used to benchmark productivity opportunities in operational performance by comparing case mix adjusted length of stay across the four stakeholder trusts and with their comparator trusts across England. In the first phase, the McKinsey team identified over £500m of potential productivity improvements, drawing on both the HES analysis as well as detailed benchmarking of the trusts’ financial accounts. In the second phase, McKinsey worked with stakeholders to develop ‘proof of concept’ projects across elective orthopaedics, end of life care, and bank and agency staff costs. For each of these categories of spend, the team undertook a productivity diagnosis and developed a joint implementation plan based on the findings. HES data was used to measure historic variation in elective orthopaedics around case-mix adjusted length of stay, procedures per pseudonymised consultant, and other activity metrics. The McKinsey team used this benchmarking analysis, presented in aggregated PowerPoint tables to convene working groups of clinicians and non-clinicians involved in elective orthopaedic care to agree upon a best practice pathway to reduce unwarranted variation. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. McKinsey completed a 3 month study in March 2016 to develop a single hospital service across a financially challenged health economy in the North of England currently served by three large trusts. The region was facing high levels of health inequalities and poor outcomes. The purpose of the project was to improve clinical care through the reduction of variation in quality, outcomes, patient experience and cost through the consolidation of independent services into a single service. HES data was critical for identifying addressable variations in length of stay, volumes and productivity across the hospitals and through benchmarking with peer trusts. Data was presented in aggregated tables comparing activity across the hospital sites, alongside data on clinical outcomes from the national clinical audits. These were used to align stakeholders around the value of the single hospital service. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 4. McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10 week delivery phase, the consortium supported hospital divisions to develop a strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. HES benchmarking against comparator peers was used to assess and strengthen the plans. The team’s work helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total in-year savings of £79m. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 5.McKinsey and Company has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. After successful pilot implementation, impact was validated using actual observed pilot data, confirming operational formats and leading to the roll-out of the operating model across the whole of the ambulance service. The project is set to continue in 2017. 6. McKinsey and Company completed a 14 week project up to December 2016 to provide financial recovery, improvement and sustainability support to an STP footprint in the East of England. The health region was facing a projected deficit of close to £500m by 2021 and is one of the most financially challenged health economies in the country. The project team used HES data to drive operational performance benchmarking of the CCG’s historic performance against comparator CCGs, and to compare internal activity trends, such as rates of A&E attendances and outpatient attendances by GP practice. The team used these benchmarks to assess the size of the total opportunity to improve, evaluate the ambition of current QIPP schemes, identify new opportunities for efficiency savings, and to support development of detailed delivery plans to implement these schemes and address remaining financial gap. 7. At the end of 2016, McKinsey and Company completed an 18 month strategic partnership with a London CCG. The objectives of the work were to ensure the organisational priorities were correct given the changing landscape, local health needs and the quality and performance of local services, as a basis for organisational work to ensure the organisation had the capacity and capability to deliver these priorities. HES data was used at the outset of the programme to conduct a broad and detailed diagnostic of historic 3 year trends in activity across acute and emergency care, and paediatric and maternity care to understand activity growth and service needs across the borough; this included analysis such as identifying potentially avoidable hospitalisations and A&E visits by age group. This diagnostic phase helped facilitate a thorough review of the priorities and draft strategic plans based upon the comprehensive analysis of service utilisation patterns. This work led to the development of 5 year commissioning strategic priorities that address the CCG’s greatest health challenges, and then informed a broader organisational development programme, which included an organisational review of the CCG’s commissioning functions and structure Expected future benefits for individual projects vary, but in almost all cases involve identification and quantification of opportunities to improve the quality of patient care and population health, and to deliver more effective, efficient care. Target dates (for expected improvements) also vary but in almost all cases are within 3 years and often include within year opportunities for service improvements and/or savings. Examples of the benefits expected for the projects described above are: Examples of the benefits expected for the seven projects described above: 1. The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks. 2. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity. 3. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year. 4. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised. 5. The ambulance service has adopted a new operational model developed following a diagnostic of deteriorating operational performance informed by benchmarking using HES A&E data. It is expected that the ambulance service will roll-out the necessary operational changes over the coming months, resulting in a significantly higher productivity of current staff and more consistent achievement of the nationally mandated access targets. 6. The outputs of the overall STP project, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of >200 STP wide initiatives, include an expected savings of ~£40m at the end of the fiscal year. 7. It is expected that this CCG’s 5 year plan, informed by the HES-derived benchmarks, and the improved capacity and capabilities within the CCG will lead to better allocative efficiency of resources, which should in turn improve access to quality care tailored to the population’s needs.
MEDEANALYTICS INTERNATIONAL LIMITED	MEDEANALYTICS INTERNATIONAL LIMITED	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The objective is to provide MedeAnalytics International Limited (“MedeAnalytics”) customers with national comparators for a range of quality and performance metrics that are derived within the MedeAnalytics UKMede system that uses HES data. The record level HES data is not linked with any other data. MedeAnalytics provides an online service to customers that are limited to clinical commissioning groups, care quality commission registered providers and public health departments, accepting data, storing it in the central repository, then providing online analytics and reporting services. Where a client of MedeAnalytics is an independent sector provider, data from NHS Digital can only be used in support of their NHS-commissioned work. In addition, the system is going to be used by customers in the Isle of Man (limited to the department of health, hospitals, community providers, and general practitioners). They are using the system to understand the performance of their health system and its relationship with the mainland NHS. The users on the Isle of Man are limited to those public organisations that come under the remit of the Isle of Man government Department of Health and Social Care. Use cases supported by the MedeAnalytics system include commissioning activities, operational and financial activities, comparators and indicators, case identification, data quality validation, and the informing of direct patient care support. By using HES data to compare activity from one region to another MedeAnalytics’ customers use the UK Mede platform to identify clinical domains where they are significant outliers. This enables them to prioritise their service redesign activities. HES is also used to produce baselines for a number of outcome metrics (eg amputations, acute kidney injury, or myocardial infarctions) that are derived from HES, these are then used for outcome based contracts. The platform based on HES is used by operational, clinical and financial staff, to inform the better use of services and resources, and ultimately better patient outcomes. In some circumstances the customers use third parties to analyse data on the system – they might include individual consultants, contracted staff or consultancy firms who have a contract with MedeAnalytics’ customer and are therefore acting as their agents. Any access to data by MedeAnalytics' customers is only ever access to aggregated data, with small numbers suppressed in line with the ICO anonymisation standard.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level ie aggregated data (with small numbers suppressed in line with the ICO anonymisation standard), users do not have access to event level or person level data. HES data is accepted into MedeAnalytics’ secure, FTP service (which is accessible from an N3 connection). On landing, initial data quality checks are undertaken (e.g.: to ensure that the correct number of records have been received, that it is not a duplicate transmission). Upon successful download of data, the ETL (Extract, Transform and Load) process is run against the data to receive, normalise and upload the Data into MedeAnalytics' central databases. ETL processing includes the following: • Extract (data receipt/collection, cleanse, parse and pre-processing) • Transform (aggregation, normalise, apply business rules) • Load (OLAP cube processing and database load) • Data integrity and reconciliation (pre and post ETL) • Performance tuning (DB indexing and data cache) • Trending archive and meta data repository and file back-up management • Production testing and QA During Transformation processing, algorithms are run to create derived data from the data stream. Derived fields are stored alongside the original data as additional fields that allow different levels of obfuscation based upon Roles Based Access Controls (RBAC). The retention period is the current NHS year plus five previous years for historical comparisons. At the end of the retention period, MedeAnalytics removes expired data and can provide appropriate destruction certificates. The reason that a longer period is needed is to provide a robust timeline with at least 4 data points to establish the baseline and trend for the management of outcome-based contracts. Three years would provide only 2 data points and the confidence limits are too wide to be useful. Our statisticians have advised us that 4 data points is the minimum. MedeAnalytics can confirm that it has complied with all previous data deletion requests. Please note: MedeAnalytics will only process data in the Back up/Disaster Recovery Centre in the event of a disaster that renders the primary data centre inoperable, and occasionally (no more than once per year) to test that the back-up/Disaster Recovery system is functional. Normal data processing is performed at the primary data centre.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level i.e. aggregated data, users do not have access to event level or person level data. Where analysis produces small numbers these are suppressed in line with the ICO anonymisation standard. The outputs derived from HES data allow comparisons between the customer's organisation or population and others nationally for a range of performance metrics such as lengths of stay, emergency admissions, A&E attendance etc. These National comparators are used by NHS organisations to improve the quality of care delivered by comparing their performance as set out by a specific range of care quality and performance measures, detailed activity and cost reports. The comparators are also used in service redesign and Health Needs Assessment (identifying underlying disease prevalence within the local population compared with the national picture). A customer typically looks at areas of activity that they are outliers for and use these as a way of prioritising service redesign activity and to target areas of deeper analysis and service improvement using more detailed data sources. As the platform allows for self service analytics MedeAnalytics cannot give a comprehensive list of all the commissioning purposes the system is used for however some examples of how the system is being used are included. Usage of the MedeAnalytics Solution is governed under the UK Data Protection Act and NHS regulations and guidance (including the Care Act) as well as the specific terms of the contracts entered into between MedeAnalytics and its clients. 1. Work done in Hertfordshire on different rates of admission for respiratory conditions by geographical area compared with national data which is informing service changes to the respiratory service in 2016. 2. On-going work looking at the number and rate of traffic accidents involving pedal cyclists that result in an admission to hospital (often under-reported to police) 3. East and North Hertfordshire CCG GPs have a report that allows them to see their referral rates and emergency admission rates compares with national averages for different conditions. This is an ongoing project. 4. The Hertfordshire Safeguarding from Children Board use an operational report on under 18 admission rates for mental health conditions, self-harm, substance misuse, and injuries 5. Gloucestershire CCG use of Right Care Peers comparators looking at 10 core HES based metrics derived from UK Mede including: follow up to first outpatient ratio, percentage elective conversions, readmissions in 30 days, and inappropriate admissions. Most of these have specific targets to reduce in the Gloucestershire CCG area. In addition, the data is used to produce comparative baselines of outcomes and will enable commissioners and providers to identify clinical areas to prioritise and to take the first steps on the path to outcomes-based contracts. An example is the East Staffordshire CCG outcome-based contract that was due to go live in April 2016 and is currently subject to ministerial review. The HES based MedeAnalytics system will be used to set baselines in these contracts (often contracts of five or more years). Performance is measured against these historical baselines and the trends calculated to drive payment. Baseline trends used to set trajectories must be robust (i.e. avoiding spikes or dips in the data due to changes in coding practices for example). When creating a best-fit line (regression line) for trajectory setting, an absolute minimum of three complete years of baseline data are required to have a reasonable degree of confidence, although five years of baseline data are preferred to ensure accurate trend lines, to allow for evaluation of statistical significance of year-on-year changes, and associated confidence intervals. This is essential when setting outcomes-based contract trajectories as the extent to which true change is expected to occur must be determined. Outputs from the system are used by clinical, financial and operational staff, across all levels including management, and are frequently used in board papers. Live access to the MedeAnalytics system (primarily through mobile devices) is used during board meetings to support operational decisions and answer live questions. HES (or HES-derived) data presented via the tool complies with the ICO anonymisation standard. Access is limited to UK users (England, Northern Ireland, Scotland and Wales) by browser location controls. IP addresses not registered in the UK are blocked from accessing the system. Access to Isle of Man users, will also be permitted under this agreement, as if the Isle of Man were part of the UK. The data items that users are able to access depend on each individual user's rights and the multi-dimensional role to which they are assigned. This means that users have access only to the relevant subset(s) of data contained in the tool. Update East Staffs CCG outcome-based contract due to go live April 2016 - the contract did not go live in April 2016 as expected, and is subject to continuing contract discussions between Virgin and East Staffs. Once those discussions complete, MediAnalytics expect to resurrect this activity.	The benefits of having the national comparators derived from the HES data available within the MedeAnalytics system are realised through the additional information they provide to support decision making by commissioners and providers in a range of activities. Commenting on the benefits of the UK Mede platform, the chair of the East of England consortium of CCGs said The challenges facing healthcare commissioners and providers are well documented, and are now demanding necessarily highly advanced and precise levels of insight to support the management and delivery of services. The CCGs need to know which areas to prioritise and what services interventions are likely to yield the greatest benefit for their populations. A self-service comparative platform allows them to do this in a wide range of service areas. The CCG consortium's commissioners are actively using HES derived insights delivered through the Mede platform to feed an enhanced analysis and understanding of provider activity. They are evaluating trends to be able to better focus on priorities and opportunities to improve demand management and patient outcomes. Delivered through this interface local health economy leaders are able to exploit fully the business intelligence benefits available from HES. Often the HES-based work is used at a project initiation stage or for ongoing reporting of impact of a specific project. It is difficult to give a comprehensive list of benefits that have derived from the many different uses of the system but some illustrative examples are: Historical Benefits: 1. In Hertfordshire using the HES-based national comparison system, it was realised that the care of frail and elderly needed to be a priority in the area and this led to a highly regarded vanguard project being initiated, Home First, that looks to improve the care of patients who are resident in care homes. This has recently been shown to have led to a significant reduction in emergency admissions and A&E attendance. The health economics analysis of the project is currently being completed. Without the initial comparator work being done on the MedeAnalytics system the project may not have been started. In September 2016, NHS England plan to use this example and apply it to other areas of the country by sharing the methodology and approach that was used by Hertfordshire including the original benchmarking work using the HES-based MedeAnalytics platform. 2. In West Essex, comparison work was done using the system as part of its success regime programme that identified that a priority area was low value interventions This came from analysis of 1-day length of stay and an analysis of inappropriate condition attending A&E. This has led to further work by the CCG to give access to GPs more real time information on admissions and A&E attenders and an associated quality improvement programme. In addition analysis using the system was used to initiate the Integrated Frailty Programme under the West Essex Better Care Fund and the Urgent Care Strategy. These programmes are subject to evaluation in 2017 and 2018. Future Benefits: The specific benefits of shifting to an outcome-based contract are well documented in the Five Year Forward View. The East Staffordshire contract aims, through a lead provider contract, to target behaviours and service design activity to improve outcomes across a number of clinical domains including diabetes and frail and elderly. The outcomes are monitoring using the HES-based MedeAnalytics platform. This contract was due to start in April 2016. MedeAnalytics are currently in discussions with other areas that are looking to adopt such an approach. OPPORTUNITY ANALYSIS FOR SERVICE RECONFIGURATION & STRATIFICATION HERTS VALLEYS CCG, October 2016 Purpose: To provide routine benchmarked analytics to commissioners to help target reconfiguration initiatives, specifically the Health, Social and Community hub The Hub in Borehamwood, Hertsmere within the context of Your Care Your Future (YCYF), the strategy for a healthier West Hertfordshire. Output: A suite of benchmarked indicators, aligned with the NHS Outcome Framework, that will support the targeting of geographies and populations for service redesign. For example, the users will evaluate standardised admission ratios by condition groups to best understand relative need in their population and allocate resources appropriately, with a supporting evidence base. Additionally, using the same metrics users can evaluate the efficacy of interventions as part of a baseline study. Benefit: Although the introduction of integrated hubs is still underway, the selection of pilot sites was informed by these analyses. MedeAnalytics will be used to evaluate the ongoing benefits of the hubs. Users fed back that showing the number of benchmarked A&E admissions (and A&E attendances in the next analysis) from specific west Herts geographical locations in a heat map, will enable CCGs and Providers to direct finite health and social care (public health) resources more efficiently and effectively. RISK ADJUSTMENT & STRATIFICATION CONTINUAL DEVELOPMENT, CALIBRATION AND APPLICATION ALL CLIENTS Purpose: MedeAnalytics is continually being asked to develop new risk adjustment and stratification models against the national dataset for a variety of outcomes/events. The purpose of these include: • the calibration and updating of existing national models (e.g. PARR30) to reflect the most recent patterns of activity and coding practice. this is routinely carried out for MedeAnalytics’ clients • develop bespoke indirectly standardised hospital mortality rates based on unique risk models derived from the national dataset • apply risk models from the national dataset against local datasets to facilitate case finding and performance management • evaluate complex preventative interventions Output: Routinely updated suite of condition-specific, and all-cause risk adjustment models for a number of outcomes (e.g. admissions to hospital, readmissions, mortality, long lengths of stay etc.). These are applied through performance management reporting, evaluation and baseline study reports, and through case finding in an operational context. Benefit: Users can better understand variation in their system, and make comparisons between populations and organisations in a fair and meaningful way with a greater understanding of what normal is. This supports routine opportunity analyses that they carry out in order to best target resources and best understand which activities have had a genuine benefit, and helped reduce costs to the system. In addition, the platform provides access to comprehensive supporting information that commissioning organisations such as Clinical Commissioning Groups use to ensure that the services they commission are: • deliver the best outcomes for their patients • designed to cater for and meet the needs of the population they are responsible for; • monitor condition prevalence within the population • identify health inequalities and work with local organisations and agencies to remove them For Acute Trusts and other care providers, it provides access to comprehensive supporting information that helps to: • ensure that the services they provide are of high quality, efficient and effective; • plan and re-engineer services to meet the changing requirements and developments in technology; Direct measurement of the benefits associated with an enabling self-service system such as this is challenging, however, proxies can be provided through use metrics (number of individual users and frequency of use) as well as examples of decisions made by customers in the management and delivery of their services that have been supported by reports / information from the Mede tool. Update 1. The Initial Comparator work was presented by East and North Herts CCG; MediAnalytics have no update whether or not they have decided to take this forward, but understand there was a keenness to do so, especially as the activity was explicitly called out in the National Data Guardian's latest report published last summer. 2. Programmes subject to evaluation 2017/2018 - there are no further dates yet, this is work that is happening at the moment, and expected to continue through this year and next.
MEDEANALYTICS INTERNATIONAL LIMITED	MEDEANALYTICS INTERNATIONAL LIMITED	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The objective is to provide MedeAnalytics International Limited (“MedeAnalytics”) customers with national comparators for a range of quality and performance metrics that are derived within the MedeAnalytics UKMede system that uses HES data. The record level HES data is not linked with any other data. MedeAnalytics provides an online service to customers that are limited to clinical commissioning groups, care quality commission registered providers and public health departments, accepting data, storing it in the central repository, then providing online analytics and reporting services. Where a client of MedeAnalytics is an independent sector provider, data from NHS Digital can only be used in support of their NHS-commissioned work. In addition, the system is going to be used by customers in the Isle of Man (limited to the department of health, hospitals, community providers, and general practitioners). They are using the system to understand the performance of their health system and its relationship with the mainland NHS. The users on the Isle of Man are limited to those public organisations that come under the remit of the Isle of Man government Department of Health and Social Care. Use cases supported by the MedeAnalytics system include commissioning activities, operational and financial activities, comparators and indicators, case identification, data quality validation, and the informing of direct patient care support. By using HES data to compare activity from one region to another MedeAnalytics’ customers use the UK Mede platform to identify clinical domains where they are significant outliers. This enables them to prioritise their service redesign activities. HES is also used to produce baselines for a number of outcome metrics (eg amputations, acute kidney injury, or myocardial infarctions) that are derived from HES, these are then used for outcome based contracts. The platform based on HES is used by operational, clinical and financial staff, to inform the better use of services and resources, and ultimately better patient outcomes. In some circumstances the customers use third parties to analyse data on the system – they might include individual consultants, contracted staff or consultancy firms who have a contract with MedeAnalytics’ customer and are therefore acting as their agents. Any access to data by MedeAnalytics' customers is only ever access to aggregated data, with small numbers suppressed in line with the ICO anonymisation standard.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level ie aggregated data (with small numbers suppressed in line with the ICO anonymisation standard), users do not have access to event level or person level data. HES data is accepted into MedeAnalytics’ secure, FTP service (which is accessible from an N3 connection). On landing, initial data quality checks are undertaken (e.g.: to ensure that the correct number of records have been received, that it is not a duplicate transmission). Upon successful download of data, the ETL (Extract, Transform and Load) process is run against the data to receive, normalise and upload the Data into MedeAnalytics' central databases. ETL processing includes the following: • Extract (data receipt/collection, cleanse, parse and pre-processing) • Transform (aggregation, normalise, apply business rules) • Load (OLAP cube processing and database load) • Data integrity and reconciliation (pre and post ETL) • Performance tuning (DB indexing and data cache) • Trending archive and meta data repository and file back-up management • Production testing and QA During Transformation processing, algorithms are run to create derived data from the data stream. Derived fields are stored alongside the original data as additional fields that allow different levels of obfuscation based upon Roles Based Access Controls (RBAC). The retention period is the current NHS year plus five previous years for historical comparisons. At the end of the retention period, MedeAnalytics removes expired data and can provide appropriate destruction certificates. The reason that a longer period is needed is to provide a robust timeline with at least 4 data points to establish the baseline and trend for the management of outcome-based contracts. Three years would provide only 2 data points and the confidence limits are too wide to be useful. Our statisticians have advised us that 4 data points is the minimum. MedeAnalytics can confirm that it has complied with all previous data deletion requests. Please note: MedeAnalytics will only process data in the Back up/Disaster Recovery Centre in the event of a disaster that renders the primary data centre inoperable, and occasionally (no more than once per year) to test that the back-up/Disaster Recovery system is functional. Normal data processing is performed at the primary data centre.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level i.e. aggregated data, users do not have access to event level or person level data. Where analysis produces small numbers these are suppressed in line with the ICO anonymisation standard. The outputs derived from HES data allow comparisons between the customer's organisation or population and others nationally for a range of performance metrics such as lengths of stay, emergency admissions, A&E attendance etc. These National comparators are used by NHS organisations to improve the quality of care delivered by comparing their performance as set out by a specific range of care quality and performance measures, detailed activity and cost reports. The comparators are also used in service redesign and Health Needs Assessment (identifying underlying disease prevalence within the local population compared with the national picture). A customer typically looks at areas of activity that they are outliers for and use these as a way of prioritising service redesign activity and to target areas of deeper analysis and service improvement using more detailed data sources. As the platform allows for self service analytics MedeAnalytics cannot give a comprehensive list of all the commissioning purposes the system is used for however some examples of how the system is being used are included. Usage of the MedeAnalytics Solution is governed under the UK Data Protection Act and NHS regulations and guidance (including the Care Act) as well as the specific terms of the contracts entered into between MedeAnalytics and its clients. 1. Work done in Hertfordshire on different rates of admission for respiratory conditions by geographical area compared with national data which is informing service changes to the respiratory service in 2016. 2. On-going work looking at the number and rate of traffic accidents involving pedal cyclists that result in an admission to hospital (often under-reported to police) 3. East and North Hertfordshire CCG GPs have a report that allows them to see their referral rates and emergency admission rates compares with national averages for different conditions. This is an ongoing project. 4. The Hertfordshire Safeguarding from Children Board use an operational report on under 18 admission rates for mental health conditions, self-harm, substance misuse, and injuries 5. Gloucestershire CCG use of Right Care Peers comparators looking at 10 core HES based metrics derived from UK Mede including: follow up to first outpatient ratio, percentage elective conversions, readmissions in 30 days, and inappropriate admissions. Most of these have specific targets to reduce in the Gloucestershire CCG area. In addition, the data is used to produce comparative baselines of outcomes and will enable commissioners and providers to identify clinical areas to prioritise and to take the first steps on the path to outcomes-based contracts. An example is the East Staffordshire CCG outcome-based contract that was due to go live in April 2016 and is currently subject to ministerial review. The HES based MedeAnalytics system will be used to set baselines in these contracts (often contracts of five or more years). Performance is measured against these historical baselines and the trends calculated to drive payment. Baseline trends used to set trajectories must be robust (i.e. avoiding spikes or dips in the data due to changes in coding practices for example). When creating a best-fit line (regression line) for trajectory setting, an absolute minimum of three complete years of baseline data are required to have a reasonable degree of confidence, although five years of baseline data are preferred to ensure accurate trend lines, to allow for evaluation of statistical significance of year-on-year changes, and associated confidence intervals. This is essential when setting outcomes-based contract trajectories as the extent to which true change is expected to occur must be determined. Outputs from the system are used by clinical, financial and operational staff, across all levels including management, and are frequently used in board papers. Live access to the MedeAnalytics system (primarily through mobile devices) is used during board meetings to support operational decisions and answer live questions. HES (or HES-derived) data presented via the tool complies with the ICO anonymisation standard. Access is limited to UK users (England, Northern Ireland, Scotland and Wales) by browser location controls. IP addresses not registered in the UK are blocked from accessing the system. Access to Isle of Man users, will also be permitted under this agreement, as if the Isle of Man were part of the UK. The data items that users are able to access depend on each individual user's rights and the multi-dimensional role to which they are assigned. This means that users have access only to the relevant subset(s) of data contained in the tool. Update East Staffs CCG outcome-based contract due to go live April 2016 - the contract did not go live in April 2016 as expected, and is subject to continuing contract discussions between Virgin and East Staffs. Once those discussions complete, MediAnalytics expect to resurrect this activity.	The benefits of having the national comparators derived from the HES data available within the MedeAnalytics system are realised through the additional information they provide to support decision making by commissioners and providers in a range of activities. Commenting on the benefits of the UK Mede platform, the chair of the East of England consortium of CCGs said The challenges facing healthcare commissioners and providers are well documented, and are now demanding necessarily highly advanced and precise levels of insight to support the management and delivery of services. The CCGs need to know which areas to prioritise and what services interventions are likely to yield the greatest benefit for their populations. A self-service comparative platform allows them to do this in a wide range of service areas. The CCG consortium's commissioners are actively using HES derived insights delivered through the Mede platform to feed an enhanced analysis and understanding of provider activity. They are evaluating trends to be able to better focus on priorities and opportunities to improve demand management and patient outcomes. Delivered through this interface local health economy leaders are able to exploit fully the business intelligence benefits available from HES. Often the HES-based work is used at a project initiation stage or for ongoing reporting of impact of a specific project. It is difficult to give a comprehensive list of benefits that have derived from the many different uses of the system but some illustrative examples are: Historical Benefits: 1. In Hertfordshire using the HES-based national comparison system, it was realised that the care of frail and elderly needed to be a priority in the area and this led to a highly regarded vanguard project being initiated, Home First, that looks to improve the care of patients who are resident in care homes. This has recently been shown to have led to a significant reduction in emergency admissions and A&E attendance. The health economics analysis of the project is currently being completed. Without the initial comparator work being done on the MedeAnalytics system the project may not have been started. In September 2016, NHS England plan to use this example and apply it to other areas of the country by sharing the methodology and approach that was used by Hertfordshire including the original benchmarking work using the HES-based MedeAnalytics platform. 2. In West Essex, comparison work was done using the system as part of its success regime programme that identified that a priority area was low value interventions This came from analysis of 1-day length of stay and an analysis of inappropriate condition attending A&E. This has led to further work by the CCG to give access to GPs more real time information on admissions and A&E attenders and an associated quality improvement programme. In addition analysis using the system was used to initiate the Integrated Frailty Programme under the West Essex Better Care Fund and the Urgent Care Strategy. These programmes are subject to evaluation in 2017 and 2018. Future Benefits: The specific benefits of shifting to an outcome-based contract are well documented in the Five Year Forward View. The East Staffordshire contract aims, through a lead provider contract, to target behaviours and service design activity to improve outcomes across a number of clinical domains including diabetes and frail and elderly. The outcomes are monitoring using the HES-based MedeAnalytics platform. This contract was due to start in April 2016. MedeAnalytics are currently in discussions with other areas that are looking to adopt such an approach. OPPORTUNITY ANALYSIS FOR SERVICE RECONFIGURATION & STRATIFICATION HERTS VALLEYS CCG, October 2016 Purpose: To provide routine benchmarked analytics to commissioners to help target reconfiguration initiatives, specifically the Health, Social and Community hub The Hub in Borehamwood, Hertsmere within the context of Your Care Your Future (YCYF), the strategy for a healthier West Hertfordshire. Output: A suite of benchmarked indicators, aligned with the NHS Outcome Framework, that will support the targeting of geographies and populations for service redesign. For example, the users will evaluate standardised admission ratios by condition groups to best understand relative need in their population and allocate resources appropriately, with a supporting evidence base. Additionally, using the same metrics users can evaluate the efficacy of interventions as part of a baseline study. Benefit: Although the introduction of integrated hubs is still underway, the selection of pilot sites was informed by these analyses. MedeAnalytics will be used to evaluate the ongoing benefits of the hubs. Users fed back that showing the number of benchmarked A&E admissions (and A&E attendances in the next analysis) from specific west Herts geographical locations in a heat map, will enable CCGs and Providers to direct finite health and social care (public health) resources more efficiently and effectively. RISK ADJUSTMENT & STRATIFICATION CONTINUAL DEVELOPMENT, CALIBRATION AND APPLICATION ALL CLIENTS Purpose: MedeAnalytics is continually being asked to develop new risk adjustment and stratification models against the national dataset for a variety of outcomes/events. The purpose of these include: • the calibration and updating of existing national models (e.g. PARR30) to reflect the most recent patterns of activity and coding practice. this is routinely carried out for MedeAnalytics’ clients • develop bespoke indirectly standardised hospital mortality rates based on unique risk models derived from the national dataset • apply risk models from the national dataset against local datasets to facilitate case finding and performance management • evaluate complex preventative interventions Output: Routinely updated suite of condition-specific, and all-cause risk adjustment models for a number of outcomes (e.g. admissions to hospital, readmissions, mortality, long lengths of stay etc.). These are applied through performance management reporting, evaluation and baseline study reports, and through case finding in an operational context. Benefit: Users can better understand variation in their system, and make comparisons between populations and organisations in a fair and meaningful way with a greater understanding of what normal is. This supports routine opportunity analyses that they carry out in order to best target resources and best understand which activities have had a genuine benefit, and helped reduce costs to the system. In addition, the platform provides access to comprehensive supporting information that commissioning organisations such as Clinical Commissioning Groups use to ensure that the services they commission are: • deliver the best outcomes for their patients • designed to cater for and meet the needs of the population they are responsible for; • monitor condition prevalence within the population • identify health inequalities and work with local organisations and agencies to remove them For Acute Trusts and other care providers, it provides access to comprehensive supporting information that helps to: • ensure that the services they provide are of high quality, efficient and effective; • plan and re-engineer services to meet the changing requirements and developments in technology; Direct measurement of the benefits associated with an enabling self-service system such as this is challenging, however, proxies can be provided through use metrics (number of individual users and frequency of use) as well as examples of decisions made by customers in the management and delivery of their services that have been supported by reports / information from the Mede tool. Update 1. The Initial Comparator work was presented by East and North Herts CCG; MediAnalytics have no update whether or not they have decided to take this forward, but understand there was a keenness to do so, especially as the activity was explicitly called out in the National Data Guardian's latest report published last summer. 2. Programmes subject to evaluation 2017/2018 - there are no further dates yet, this is work that is happening at the moment, and expected to continue through this year and next.
MEDEANALYTICS INTERNATIONAL LIMITED	MEDEANALYTICS INTERNATIONAL LIMITED	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The objective is to provide MedeAnalytics International Limited (“MedeAnalytics”) customers with national comparators for a range of quality and performance metrics that are derived within the MedeAnalytics UKMede system that uses HES data. The record level HES data is not linked with any other data. MedeAnalytics provides an online service to customers that are limited to clinical commissioning groups, care quality commission registered providers and public health departments, accepting data, storing it in the central repository, then providing online analytics and reporting services. Where a client of MedeAnalytics is an independent sector provider, data from NHS Digital can only be used in support of their NHS-commissioned work. In addition, the system is going to be used by customers in the Isle of Man (limited to the department of health, hospitals, community providers, and general practitioners). They are using the system to understand the performance of their health system and its relationship with the mainland NHS. The users on the Isle of Man are limited to those public organisations that come under the remit of the Isle of Man government Department of Health and Social Care. Use cases supported by the MedeAnalytics system include commissioning activities, operational and financial activities, comparators and indicators, case identification, data quality validation, and the informing of direct patient care support. By using HES data to compare activity from one region to another MedeAnalytics’ customers use the UK Mede platform to identify clinical domains where they are significant outliers. This enables them to prioritise their service redesign activities. HES is also used to produce baselines for a number of outcome metrics (eg amputations, acute kidney injury, or myocardial infarctions) that are derived from HES, these are then used for outcome based contracts. The platform based on HES is used by operational, clinical and financial staff, to inform the better use of services and resources, and ultimately better patient outcomes. In some circumstances the customers use third parties to analyse data on the system – they might include individual consultants, contracted staff or consultancy firms who have a contract with MedeAnalytics’ customer and are therefore acting as their agents. Any access to data by MedeAnalytics' customers is only ever access to aggregated data, with small numbers suppressed in line with the ICO anonymisation standard.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level ie aggregated data (with small numbers suppressed in line with the ICO anonymisation standard), users do not have access to event level or person level data. HES data is accepted into MedeAnalytics’ secure, FTP service (which is accessible from an N3 connection). On landing, initial data quality checks are undertaken (e.g.: to ensure that the correct number of records have been received, that it is not a duplicate transmission). Upon successful download of data, the ETL (Extract, Transform and Load) process is run against the data to receive, normalise and upload the Data into MedeAnalytics' central databases. ETL processing includes the following: • Extract (data receipt/collection, cleanse, parse and pre-processing) • Transform (aggregation, normalise, apply business rules) • Load (OLAP cube processing and database load) • Data integrity and reconciliation (pre and post ETL) • Performance tuning (DB indexing and data cache) • Trending archive and meta data repository and file back-up management • Production testing and QA During Transformation processing, algorithms are run to create derived data from the data stream. Derived fields are stored alongside the original data as additional fields that allow different levels of obfuscation based upon Roles Based Access Controls (RBAC). The retention period is the current NHS year plus five previous years for historical comparisons. At the end of the retention period, MedeAnalytics removes expired data and can provide appropriate destruction certificates. The reason that a longer period is needed is to provide a robust timeline with at least 4 data points to establish the baseline and trend for the management of outcome-based contracts. Three years would provide only 2 data points and the confidence limits are too wide to be useful. Our statisticians have advised us that 4 data points is the minimum. MedeAnalytics can confirm that it has complied with all previous data deletion requests. Please note: MedeAnalytics will only process data in the Back up/Disaster Recovery Centre in the event of a disaster that renders the primary data centre inoperable, and occasionally (no more than once per year) to test that the back-up/Disaster Recovery system is functional. Normal data processing is performed at the primary data centre.	The MedeAnalytics system provides information to a range of NHS and Social Care staff (including commissioners, service managers and clinicians, with responsibilities for operational, financial and clinical activities). The MedeAnalytics UK Mede system is a platform that is run on HES to produce graphs, charts, reports and dashboards specifically geared to the needs of each user. The outputs of the system are at a summary level i.e. aggregated data, users do not have access to event level or person level data. Where analysis produces small numbers these are suppressed in line with the ICO anonymisation standard. The outputs derived from HES data allow comparisons between the customer's organisation or population and others nationally for a range of performance metrics such as lengths of stay, emergency admissions, A&E attendance etc. These National comparators are used by NHS organisations to improve the quality of care delivered by comparing their performance as set out by a specific range of care quality and performance measures, detailed activity and cost reports. The comparators are also used in service redesign and Health Needs Assessment (identifying underlying disease prevalence within the local population compared with the national picture). A customer typically looks at areas of activity that they are outliers for and use these as a way of prioritising service redesign activity and to target areas of deeper analysis and service improvement using more detailed data sources. As the platform allows for self service analytics MedeAnalytics cannot give a comprehensive list of all the commissioning purposes the system is used for however some examples of how the system is being used are included. Usage of the MedeAnalytics Solution is governed under the UK Data Protection Act and NHS regulations and guidance (including the Care Act) as well as the specific terms of the contracts entered into between MedeAnalytics and its clients. 1. Work done in Hertfordshire on different rates of admission for respiratory conditions by geographical area compared with national data which is informing service changes to the respiratory service in 2016. 2. On-going work looking at the number and rate of traffic accidents involving pedal cyclists that result in an admission to hospital (often under-reported to police) 3. East and North Hertfordshire CCG GPs have a report that allows them to see their referral rates and emergency admission rates compares with national averages for different conditions. This is an ongoing project. 4. The Hertfordshire Safeguarding from Children Board use an operational report on under 18 admission rates for mental health conditions, self-harm, substance misuse, and injuries 5. Gloucestershire CCG use of Right Care Peers comparators looking at 10 core HES based metrics derived from UK Mede including: follow up to first outpatient ratio, percentage elective conversions, readmissions in 30 days, and inappropriate admissions. Most of these have specific targets to reduce in the Gloucestershire CCG area. In addition, the data is used to produce comparative baselines of outcomes and will enable commissioners and providers to identify clinical areas to prioritise and to take the first steps on the path to outcomes-based contracts. An example is the East Staffordshire CCG outcome-based contract that was due to go live in April 2016 and is currently subject to ministerial review. The HES based MedeAnalytics system will be used to set baselines in these contracts (often contracts of five or more years). Performance is measured against these historical baselines and the trends calculated to drive payment. Baseline trends used to set trajectories must be robust (i.e. avoiding spikes or dips in the data due to changes in coding practices for example). When creating a best-fit line (regression line) for trajectory setting, an absolute minimum of three complete years of baseline data are required to have a reasonable degree of confidence, although five years of baseline data are preferred to ensure accurate trend lines, to allow for evaluation of statistical significance of year-on-year changes, and associated confidence intervals. This is essential when setting outcomes-based contract trajectories as the extent to which true change is expected to occur must be determined. Outputs from the system are used by clinical, financial and operational staff, across all levels including management, and are frequently used in board papers. Live access to the MedeAnalytics system (primarily through mobile devices) is used during board meetings to support operational decisions and answer live questions. HES (or HES-derived) data presented via the tool complies with the ICO anonymisation standard. Access is limited to UK users (England, Northern Ireland, Scotland and Wales) by browser location controls. IP addresses not registered in the UK are blocked from accessing the system. Access to Isle of Man users, will also be permitted under this agreement, as if the Isle of Man were part of the UK. The data items that users are able to access depend on each individual user's rights and the multi-dimensional role to which they are assigned. This means that users have access only to the relevant subset(s) of data contained in the tool. Update East Staffs CCG outcome-based contract due to go live April 2016 - the contract did not go live in April 2016 as expected, and is subject to continuing contract discussions between Virgin and East Staffs. Once those discussions complete, MediAnalytics expect to resurrect this activity.	The benefits of having the national comparators derived from the HES data available within the MedeAnalytics system are realised through the additional information they provide to support decision making by commissioners and providers in a range of activities. Commenting on the benefits of the UK Mede platform, the chair of the East of England consortium of CCGs said The challenges facing healthcare commissioners and providers are well documented, and are now demanding necessarily highly advanced and precise levels of insight to support the management and delivery of services. The CCGs need to know which areas to prioritise and what services interventions are likely to yield the greatest benefit for their populations. A self-service comparative platform allows them to do this in a wide range of service areas. The CCG consortium's commissioners are actively using HES derived insights delivered through the Mede platform to feed an enhanced analysis and understanding of provider activity. They are evaluating trends to be able to better focus on priorities and opportunities to improve demand management and patient outcomes. Delivered through this interface local health economy leaders are able to exploit fully the business intelligence benefits available from HES. Often the HES-based work is used at a project initiation stage or for ongoing reporting of impact of a specific project. It is difficult to give a comprehensive list of benefits that have derived from the many different uses of the system but some illustrative examples are: Historical Benefits: 1. In Hertfordshire using the HES-based national comparison system, it was realised that the care of frail and elderly needed to be a priority in the area and this led to a highly regarded vanguard project being initiated, Home First, that looks to improve the care of patients who are resident in care homes. This has recently been shown to have led to a significant reduction in emergency admissions and A&E attendance. The health economics analysis of the project is currently being completed. Without the initial comparator work being done on the MedeAnalytics system the project may not have been started. In September 2016, NHS England plan to use this example and apply it to other areas of the country by sharing the methodology and approach that was used by Hertfordshire including the original benchmarking work using the HES-based MedeAnalytics platform. 2. In West Essex, comparison work was done using the system as part of its success regime programme that identified that a priority area was low value interventions This came from analysis of 1-day length of stay and an analysis of inappropriate condition attending A&E. This has led to further work by the CCG to give access to GPs more real time information on admissions and A&E attenders and an associated quality improvement programme. In addition analysis using the system was used to initiate the Integrated Frailty Programme under the West Essex Better Care Fund and the Urgent Care Strategy. These programmes are subject to evaluation in 2017 and 2018. Future Benefits: The specific benefits of shifting to an outcome-based contract are well documented in the Five Year Forward View. The East Staffordshire contract aims, through a lead provider contract, to target behaviours and service design activity to improve outcomes across a number of clinical domains including diabetes and frail and elderly. The outcomes are monitoring using the HES-based MedeAnalytics platform. This contract was due to start in April 2016. MedeAnalytics are currently in discussions with other areas that are looking to adopt such an approach. OPPORTUNITY ANALYSIS FOR SERVICE RECONFIGURATION & STRATIFICATION HERTS VALLEYS CCG, October 2016 Purpose: To provide routine benchmarked analytics to commissioners to help target reconfiguration initiatives, specifically the Health, Social and Community hub The Hub in Borehamwood, Hertsmere within the context of Your Care Your Future (YCYF), the strategy for a healthier West Hertfordshire. Output: A suite of benchmarked indicators, aligned with the NHS Outcome Framework, that will support the targeting of geographies and populations for service redesign. For example, the users will evaluate standardised admission ratios by condition groups to best understand relative need in their population and allocate resources appropriately, with a supporting evidence base. Additionally, using the same metrics users can evaluate the efficacy of interventions as part of a baseline study. Benefit: Although the introduction of integrated hubs is still underway, the selection of pilot sites was informed by these analyses. MedeAnalytics will be used to evaluate the ongoing benefits of the hubs. Users fed back that showing the number of benchmarked A&E admissions (and A&E attendances in the next analysis) from specific west Herts geographical locations in a heat map, will enable CCGs and Providers to direct finite health and social care (public health) resources more efficiently and effectively. RISK ADJUSTMENT & STRATIFICATION CONTINUAL DEVELOPMENT, CALIBRATION AND APPLICATION ALL CLIENTS Purpose: MedeAnalytics is continually being asked to develop new risk adjustment and stratification models against the national dataset for a variety of outcomes/events. The purpose of these include: • the calibration and updating of existing national models (e.g. PARR30) to reflect the most recent patterns of activity and coding practice. this is routinely carried out for MedeAnalytics’ clients • develop bespoke indirectly standardised hospital mortality rates based on unique risk models derived from the national dataset • apply risk models from the national dataset against local datasets to facilitate case finding and performance management • evaluate complex preventative interventions Output: Routinely updated suite of condition-specific, and all-cause risk adjustment models for a number of outcomes (e.g. admissions to hospital, readmissions, mortality, long lengths of stay etc.). These are applied through performance management reporting, evaluation and baseline study reports, and through case finding in an operational context. Benefit: Users can better understand variation in their system, and make comparisons between populations and organisations in a fair and meaningful way with a greater understanding of what normal is. This supports routine opportunity analyses that they carry out in order to best target resources and best understand which activities have had a genuine benefit, and helped reduce costs to the system. In addition, the platform provides access to comprehensive supporting information that commissioning organisations such as Clinical Commissioning Groups use to ensure that the services they commission are: • deliver the best outcomes for their patients • designed to cater for and meet the needs of the population they are responsible for; • monitor condition prevalence within the population • identify health inequalities and work with local organisations and agencies to remove them For Acute Trusts and other care providers, it provides access to comprehensive supporting information that helps to: • ensure that the services they provide are of high quality, efficient and effective; • plan and re-engineer services to meet the changing requirements and developments in technology; Direct measurement of the benefits associated with an enabling self-service system such as this is challenging, however, proxies can be provided through use metrics (number of individual users and frequency of use) as well as examples of decisions made by customers in the management and delivery of their services that have been supported by reports / information from the Mede tool. Update 1. The Initial Comparator work was presented by East and North Herts CCG; MediAnalytics have no update whether or not they have decided to take this forward, but understand there was a keenness to do so, especially as the activity was explicitly called out in the National Data Guardian's latest report published last summer. 2. Programmes subject to evaluation 2017/2018 - there are no further dates yet, this is work that is happening at the moment, and expected to continue through this year and next.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Office for National Statistics Mortality Data (linkable to HES)	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Patient Reported Outcome Measures (Linkable to HES)	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Mental Health Minimum Data Set	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Office for National Statistics Mortality Data (linkable to HES)	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY (MHRA)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	N	CPRD is the UK’s pre-eminent research service, providing access to anonymised (in line with the ICO code of anonymisation) primary care data linked by NHS Digital to other similarly anonymised health data provided by NHS Digital and others for the purposes of public health research including the monitoring of drug safety. All such data is linked (in its identifiable form) by NHS Digital only. It is jointly funded by the MHRA and the National Institute for Health Research (NIHR). CPRD’s aims are to support vital public health research and to inform advances in patient safety in the delivery of patient care pathways. These depend on access to accurate, real-time representative patient data to produce reliable evidence-based clinical and drug safety guidance. CPRD services are designed to maximise the way anonymised NHS clinical data can be used to improve and safeguard public health. For more than 20 years data provided by CPRD have been used in a range of drug safety and epidemiological studies that have impacted on health care, and resulted in over 1700 peer-reviewed publications. In addition to supporting high-quality observational research, CPRD is developing world-leading services based on using real world data to support clinical trials and intervention studies. The intention is to continue to link anonymised CPRD primary care data to NHS Digital’s secondary care and other datasets, as linkage greatly increases the scale, depth, completeness and therefore value of data available for public health research. The outputs of such research based on linked data in turn improve and protect patient care pathways/treatments and provide clinical benefits for the UK, supporting delivery of CPRD’s core objectives. CPRD’s research and data services are based on a database of anonymised longitudinal primary care records contributed by consenting GP practices from the four UK nations, and on the ability to link primary care data to secondary care data (and other data sets), from the NHS, Office of National Statistics (ONS) and Public Health England (PHE). One of CPRD’s main priorities is to increase the number of national data sets that are linked to primary care data and made available on a routine basis to the research community. Such collection and linkages occur under the appropriate permissions (ethical and s251), which have been granted to CPRD by the East Midlands – Derby Research Ethics Committee (REC), and the Health Research Authority (HRA). NHS Digital has been providing secondary and other data for linkage with CPRD primary care data for a number of years. Data linkage is carried out exclusively by NHS Digital as the Trusted Third Party (TTP) for this purpose. Linked data sets currently available include extracts from ONS Death Registration data; Hospital Episode Statistics (HES), which encompasses Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data; Patient Reported Outcome Measures (PROMs); Diagnostic Imaging Dataset (DID); Mental Health data; National Cancer Registry; Deprivation data including Townsend Score and Index of Multiple Deprivation. Critical care is supplied as a separate dataset by NHS Digital, but is integrated with Admitted Patient Care. Data can only be used for public health research purposes in research recommended for approval by ISAC for MHRA database research. CPRD make the final decision on access, and ensure compliance with NHS Digital’s requirements within the data sharing agreement, including (e.g.) security of the third party. Access to CPRD data and services will not be permitted in circumstances that may result in loss of public trust or for activities that may undermine the integrity of the CPRD database.	CPRD has established agreements with General Practices and agreed contracts with their data processors, the GP clinical IT system providers, enabling the extraction of agreed data from the primary care electronic health record (EHR). Protecting patient confidentiality is paramount to CPRD. A number of processes and procedures are in place to safeguard the identity and confidentiality of patient data received and supplied by CPRD. An overview of these is presented below, including minimised dataset extraction, data transformation, strong and multiple pseudonymisation, and governance and scrutiny on approvals to use linked data. The CPRD Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out the management processes employed to ensure that CPRD appropriately anonymises patient data for observational research purposes, and complies with the Information Commissioner’s Office (ICO) Code on Anonymisation and with Office of National Statistics (ONS) requirements on use of death registration data. 1) Data Collection Data collected by CPRD includes all coded patient primary care data, including gender, year-of-birth, and year-and-month-of-birth for patients aged 16 and under. CPRD does not receive patient name, address, full date of birth, NHS Number or free text medical notes. In order to enable the linking of primary care records to other health related data, GP EHR suppliers provide certain patient identifiers directly to NHS Digital. These are: NHS Number, full date of birth, post code and gender. CPRD does receive gender but does not receive any of the other identifiers. The Trusted Third Party (NHS Digital) provides the linkage service for CPRD. Data collections are received by CPRD securely through an N3 link. Data arrives as a series of incremental collections of data from practices that have agreed to share data with CPRD. Data collections, once received, are checked for content (data structure and format), completeness (presence of key and optional data files) and continuity. The collection is then archived. Details of each data collection are logged to an administrative database, and data is made available for processing. Database creation or a build process is undertaken on a monthly basis by taking a snapshot of the fully processed data and organising it into a structure which enables tools to query and extract the data for use in observation and interventional research studies. CPRD retains the data collected up to the point that a GP Practice withdraws from participation. This is to ensure that CPRD can create (if needed) datasets for (eg) validation of previous research, or for longitudinal studies. Patient opt-outs remain respected from the point of notification to CPRD. 2) Data transformation CPRD does not release the same linked data to external researchers that it receives from NHS Digital. The changes made in between data receipt and release are termed ‘data transformation’. This is done to protect patient confidentiality, and also to better facilitate relevant research. Transformation involves removing the provider codes provided by NHS Digital. Data provided by CPRD is matched to the former Strategic Health Authority boundaries. It is based on matching the address of the GP Practice to the SHA. This ‘blurs’ the link between hospital activity records and other potential identifiers collected and provided (Gender, Year of Birth, Date of Death and Ethnicity). For example, transformation of the CPRD linked HES Admitted Patient Care (APC) data involves: (i) The encrypted_”HESID” id field provided to CPRD by NHS Digital is not released to customers. CPRD creates a pseudonym linked to a unique patient activity record in the HES data. (ii) Encoding of the record level identifier (epikey). The epikey variable has been encoded by the CPRD to minimise the risk of breaching licensing conditions through linkage of these data to other HES data sources containing patient identifiable information. The epikey is encoded with a new key each time data is processed, so that the epikey for the same record differs in every release of CPRD linked HES APC data. This prevents different researchers from linking patients from the same dataset, or from comparison with older release versions of the data. (iii) Collating data across years, formatting date and diagnosis fields, and dropping fields (mainly provider and geographical based) from standard release of the data. The episode-level data files received by NHS Digital are transformed into a normalised data structure containing the following tables: 1. Hospitalisations 2. Episodes 3. Diagnosis 4. Procedures 5. Augmented Care 6. Critical Care 7. Maternity 8. Health Resource Group 3) Pseudonymisation process CPRD has agreed pseudonymisation processes with each GP EHR system provider as well as the Trusted Third Party used for data linkage. The overarching process for patient data pseudonymisation comprises of the following stages to protect patient confidentiality at all times: (i) GP system provider – the provider replaces the patient identifiers (NHS number) in each patient record with a system practice ID and system patient ID before its secure transfer to CPRD. (ii) CPRD – on collection of the patient data, CPRD replaces the original data source patient and practice ID from the GP system provider with a CPRD patient and practice pseudonym. (iii) Data linkage – where this is undertaken by the Trusted Third Party (using patient identifiers sent directly from GPs), all linked patient record data are anonymised by the TTP before release to CPRD. Similarly, where cancer registry data is received by CPRD from Public Health England for linkage, PHE anonymise patient data before release to CPRD. (iv) Data release – the linked data is cut by CPRD to minimise data ultimately released to third party researchers, with the linked patient ID replaced again by a further patient ID at release establishing yet another layer of separation. Record level identifiers (such as epikey, attendkey, aekey) are additionally encoded such that the record level pseudonym differs in every release of the linked data. Combined with the wider processes and procedures noted in this section, the above pseudonymisation process precludes users of linked data from identifying patients from the data provided. 4) Data use Access and use of the data is controlled. With the exception of interventional and clinical studies (which require separate Health Research Authority approval), researchers must gain approval for their study protocol from the Independent Scientific Advisory Committee for MHRA Database Research (ISAC). Approved applications to ISAC are published on the CPRD website https://www.cprd.com/ISAC/datause.asp. CPRD may generate aggregate level linked data without ISAC approval to inform feasibility and design of external research (observational and interventional/clinical) and for assessment of ISAC protocols. CPRD will undertake such assessments on behalf of external researchers with no release of record level linked data permitted outside of CPRD. ISAC therefore plays a key data governance role. Approval from ISAC is required if access to anonymised patient level data is requested for requested for observational research and there is an intention to publish results, or where the study depends on access to primary care data linked to other health related data. ISAC's role is to determine whether a research proposal is of public health value, will be conducted by researchers with the appropriate level of expertise, highlight if any ethical or confidentiality issues may arise in the proposed research, and to consider the scientific merit of the proposed methods and overall study. 5) Data release Release of patient level linked data to third party researchers will only occur after: a) All required approvals (including ISAC approval) have been obtained; b) Data is determined to be anonymised as per CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research; c) Additional requirements on anonymisation relating to ONS death registration data have been met, with agreement from ONS (see below); d) Researchers are provided with robust contracts defining terms of use relating to secure access, retention and destruction of the data; and e) Access to data provided by CPRD which is sub-licensed having been provided by NHS Digital, the Office for National Statistics or by any other Data Controller or Custodian, is done so under terms compatible with the terms under which data is provided to CPRD. With regard to release of ONS death registration data: • ONS death data provided by NHS Digital is stored separately by CPRD and interrogated on a case by case basis to assess the scientific value of research applications; • Sub-national geographic data are not provided to researchers without additional review and approvals from ISAC and where relevant, HRA CAG; • As standard, CPRD match each GP practice postcode to a larger geographical area aligned with the historical NHS Strategic Health Authority boundaries, ensuring an underlying population size of at least 2 million persons. The GP practice post code, the hospital or other institutional identifier are not released; • The ISAC review includes a risk assessment of patient re-identification, and if appropriate research applicants are required to outline risk mitigation plans; • CPRD’s Policy for Managing Anonymisation and the Risk of Identification in Observational Research sets out CPRD’s policy for the release for publication of data relating to small cell counts; • Restrictions on the number of stratified analyses are imposed in the case of research proposals investigating rare diseases or treatments to minimise the risk of re-identification; • ISAC approvals only allow exact ONS dates of death for use in calculating the time to death from a given event of interest (for e.g. a particular diagnosis) for the purpose of survival analyses and where there is a clear benefit to public health from the proposed research; and • Researchers are also contractually bound to maintain patient anonymity and prevent inadvertent re-identification of patients In accordance with guidance from the Information Commissioner’s Office (ICO), CPRD does not permit personal data to be processed outside its own servers, and hence any such data is retained within the EU. 6) Data Access Management CPRD processes patient data and makes it available internally to CPRD researchers. To control third-party access to linked data and minimise data released to third parties, the CPRD Observational Research Team will extract datasets for researchers against a query specification or primary care data defined cohort. The query and its output content will be agreed with the researcher prior to generation of the data sets. This is the only process by which applicants to CPRD may access linked data. Hewlett Packard/Sungard are captured as data storage addresses as for the purposes of this application, Sungard is considered to be the initial back-up and recovery, Hewlett Packard are the 'back-up to the back-up'. They are not involved in processing of the data in any way (Sungard provide a facilities management and site management service). CPRD have confirmed that neither HP nor Sungard have access to the server (neither administrative nor user rights). 7) Information Security Measures CPRD is part of a wider Government agency (the MHRA) and conforms to the 10 National Data Guardian data security standards as well as to NHS Digital requirements. The MHRA meets NHS Information Governance Toolkit standards on information security, and details on standards and arrangements are set out in CPRD’s approved System Level Security Policy (SLSP). CPRD operates to a high level to ensure that when data is transmitted and or stored it is done so in a way that protects the data. All data in CPRD is stored in a “Tier 3” data centre that is compliant with Government standards to operate in a way that meets the full requirements for managing and storing such important data. The measures are always under review and are subject to audit. Security measures include: • Multifactor authentication for access • Monitoring of access • Round the clock security staff presence • Robust firewalls and other access restrictions A back-up store of the data (provided by named data processors) mirrors the above features but in an alternative location to allow for business continuity. 8) Data destruction and disposal Data destruction standards (currently NHS Digital ‘Destruction and Disposal of Sensitive Data’ guidelines v3.2) will be met through planned implementation in MHRA of a Blancco LUN Eraser tool, to guarantee that sensitive data is properly erased and sanitized securely and permanently. This tool ensures compliance with industry standards and regulations, including PCI DSS, HIPAA, SOX, ISO 27001 and the EU General Data Protection Regulation, and the tool will be in place by August 2017. 9) Encryption Encryption is used for data in transit between secure locations. This will apply to both identifier data for linkage and clinical / research data. Although the clinical data is pseudonymised, there remains the residual risk of re-identification or the risk of inclusion of disclosive content and data is only intended for processing by authorised recipients. Encryption mitigates the risk and provides assurance. The default minimum standard for encryption will be AES 256 using a complex pass-phrase consisting of 12 characters and a mix of upper case, lower case, numeric and special characters. 10) Training All CPRD staff and licensed data users are appropriately trained and have the necessary understanding of the governance processes pertaining to relevant laws. They will also be aware that any misuse of data may result in disciplinary procedures and, in the case of a severe breach, dismissal and immediate removal from the premises. Training covering use of data is mandatory for CPRD staff and licence-holders prior to accessing data. CPRD staff who are responsible for the collection of data and interaction with site staff are precluded from access to data. Data is kept on restricted servers and drives accessible only to appropriately trained research staff. NHS Digital permits CPRD sub-licensees to share data with third parties subject to the third parties collaborating on the same research as the sub-licensee, and subject to the terms, checks and controls carried out by CPRD in relation to sub-licences. Details of such licences will be published and shared with NHS Digital.	CPRD customers using linked data products will be producing (on an on-going basis) research publications in peer-reviewed journals and presentations at scientific conferences. CPRD customers include academic institutions, pharmaceutical companies, Governmental centres and research charities. These all undertake medical and health data research, which may result in formal publications. All data included in such outputs by CPRD customers will be aggregated, small numbers suppressed in line with the HES Analysis Guide (or dataset specific suppression controls). A selection of recent publications resulting from use of CPRD linked data are presented below. Moss S, Melia J, Sutton J, Mathews C, Kirby M. (2016) ‘Prostate-specific antigen testing rates and referral patterns from general practice data in England.’ Int J Clin Pract. 2016 Apr;70(4):312-8. doi: 10.1111/ijcp.12784. Epub 2016 Mar 14. William Hollingworth, (Professor), Mousumi Biswas, Rachel L Maishman, Mark J Dayer, Theresa McDonagh, Sarah Purdya, Barnaby C Reeves, Chris A Rogers, Rachael Williams, Maria Pufulete. (2016) ‘The healthcare costs of heart failure during the last five years of life: A retrospective cohort study’ International Journal of Cardiology, Volume 224, 1 December 2016, Pages 132–138 Laurence Baril, Dominique Rosillon, Corinne Willame, Maria Genalin Angelo, Julia Zima, Judith H. van den Bosch, Tjeerd Van Staa, Rachael Boggon, Eveline M. Bunge, Sonia Hernandez-Diaz, Christina D. Chambers. (2015) ‘Risk of spontaneous abortion and other pregnancy outcomes in 15–25 year old women exposed to human papillomavirus-16/18 AS04-adjuvanted vaccine in the United Kingdom’ Vaccine, Vol 33, Issue 48, 27 November 2015, Pages 6884–6891 Taylor S, Taylor RJ, Lustig RL, Schuck-Paim C, Haguinet F, Webb DJ, Logie J, Matias G, Fleming DM (2016). ‘Modelling estimates of the burden of respiratory syncytial virus infection in children in the UK.’ BMJ Open. (2016) Jun 2;6(6):e009337. doi: 10.1136/bmjopen-2015-009337. Wing K, Bhaskaran K, Smeeth L, van Staa TP, Klungel OH, Reynolds RF, Douglas I (2016). ‘Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury.’ BMJ Open. 2016 Sep 2;6(9):e012102. doi: 10.1136/bmjopen-2016-012102. Alexandre L, Clark AB, Bhutta HY, Chan SS, Lewis MP, Hart AR. (2016) ‘Association Between Statin Use After Diagnosis of Esophageal Cancer and Survival: A Population-Based Cohort Study.’ Gastroenterology. 2016 Apr;150(4):854-65.e1; quiz e16-7. doi: 10.1053/j.gastro.2015.12.039. Epub 2016 Jan 9.	Past and existing studies (on an ongoing basis) use linked data with the CPRD primary care database to generate research results. These studies are expected to produce benefits of clinical importance to the UK public, and to be published in peer-reviewed journals and presented at scientific conferences. Some recent examples and other relevant publications resulting from linked data research which resulted in clinical benefits are presented below. Case Study 1: The effectiveness of the influenza vaccine against hospital admissions and mortality in individuals with type 2 diabetes Seasonal influenza accounts for a significant proportion of excess winter mortality. Current policy in the UK and in many countries worldwide recommends annual flu vaccinations for patients with chronic conditions such as diabetes, though evidence to support such policies is limited. Imperial College London recently investigated the effectiveness of the influenza vaccine at reducing cardiovascular and respiratory hospital admissions and mortality in patients with type 2 diabetes. The study used linkages between CPRD GOLD primary care data, Hospital Episode Statistics (HES) and the Office for National Statistics (ONS) mortality data to look at admissions and death in 125,000 patients over a seven-year period. Influenza vaccination was associated with a reduction in the rate of hospital admissions for acute cardiovascular and respiratory disease and a reduction in all-cause mortality across the seven flu seasons. The study has been widely reported within healthcare and mainstream media and supports current flu vaccination initiatives in the UK and beyond. Reference 1: Vamos EP et al. Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes. CMAJ. 2016 Oct 4;188(14):E342-E351. Case Study 2: Risk associated with the prescription of long-acting β2-agonists (LABA), short-acting β2-agonists (SABA) or inhaled corticosteroids (ICS) for asthma in primary care. Omalizumab is a recent antibody-based treatment developed to help control moderate to severe allergic asthma, when symptom control with inhaled corticosteroids (ICS) is inadequate. ICS are frequently prescribed alongside long-acting β2-agonists (LABA). A 2010 study using CPRD data (then GPRD) linked with Hospital Episode Statistics investigated the risk of asthma-related death and hospitalisation among patients on ICS or LABA therapy. The study was important to establish the relative risk across commonly-prescribed asthma treatments and concluded that LABA exposure was not associated with an increased risk for all-cause mortality. This study was subsequently incorporated into NICE guidelines released in 2013 outlining evidence-based recommendations for omalizumab use in patients with severe persistent asthma. Reference 2: de Vries F, Setakis E, Zhang B, van Staa TP. Long-acting {beta}2-agonists in adult asthma and the pattern of risk of death and severe asthma outcomes: a study using the GPRD. Eur Respir J. 2010 Sep;36(3):494-502. Additional references describing health benefits of CPRD and linked data. Example Reference 3: The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Antonis A Kousoulis, Imran Rafi, Chair, and Simon de Lusignan Br J Gen Pract. 2015 Feb; 65(631): 54–55. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325440/?tool=pmcentrez Example Reference 4: Quality Improvement's greatest hits of 2016, Hannah Price, Head of Quality Improvement http://www.rcgp.org.uk/clinical-and-research/clinical-news/quality-improvements-greatest-hits-of-2016.aspx RCGP and Clinical Practice Research Datalink (CPRD) have joined forces to produce innovative data reports focusing on prescribing and patient safety, which enable benchmarking and case-finding. Over 100 practices from all four nations of the UK have participated in the successful pilot stage of the projects. This phase is now coming to an end and the reports will be rolled out to all practices in the CPRD network during 2017. Example Reference 5: A recently published systematic review (Oyinlola et al 2016) identified 43 CPRD studies that have been used in 25 medical guidance documents. The reviewers found that use of data from the CPRD to inform guidelines has increased in recent years and noted the importance of linking data to extend research to medical conditions that are treated in multiple settings (e.g. primary and secondary care). Reference: Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res. 2016 Jul 26;16:299. Example Reference 6: A review of patients with learning disabilities (LD) at the Winterbourne View private hospital was established for all aspects of care for this patient group. This study used three years of CPRD data alongside HES APC data to describe the level of GP prescribing of psychotropic medication to patients with LD, and explored whether a relevant diagnostic indication was recorded. The results of the study led NHS England to promise rapid and sustained action to tackle over-prescribing, and an urgent letter sent to professionals to urge they review their own prescribing. Reference: Glover, G, Williams R. 'Prescribing of psychotropic drugs to people with learning disabilities and/or autism by general practitioners in England'. Public Health England. June 2015.
MEDITRENDS LTD	MEDITRENDS LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Meditrends Limited (incorporated in 2013, taking over the business of Beacon Consulting) will use the data solely for the following purposes (any other purposes will be subject to a further application):- Purpose 1): Meditrends Online Meditrends Online is a web delivered system that uses aggregated, supressed, non-sensitive, non-identifiable HES data to assist NHS commissioners throughout the commissioning cycle. Meditrends Online will allow users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The current users of Meditrends Online are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care including: a. NHS-GPs, Commissioners, Acute Trusts, Area & Regional Teams, Strategic Clinical networks; b. Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN); c. NHS England Commissioning Support Units (CSUs) 2. Patient support groups & other health related charitable organisations; 3. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology); All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI) Although the users of Meditrends Online are all of the above groups, the data can only be used for the purposes listed above, with the ultimate beneficiary in all cases being the NHS and Social Care. All users will give a written undertaking to this effect. Meditrends Online outputs will be used by the ultimate beneficiary to optimise the Commissioning Cycle in the following ways: • To assess performance against similar comparisons and to understand where change could be required to achieve Quality Innovation Productivity and Prevention (QIPP) planning. • To communicate with all stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. • To identify areas of best practice in disease management. To use this data to define, monitor and communicate critical indicators of success. • To forecast future levels of demand for services and configure local resources to meet these needs Life Science Companies are a user of the aggregated outputs exclusively for the purpose of using Meditrends Online to benefit the health and social care organisations in England. Life Science Companies users will be highly restricted in their use of Meditrends Online to ensure aggregated HES data are not used for commercial purposes such as targeting sales resource. These restrictions are underpinned through a binding legal contract which contains terms which require: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations or patient support groups & other health related charitable organisations in England • The system not to be used for solely commercial purposes • The same aggregated HES data outputs to be made available, if requested, to all health and social care organisations or patient support groups & other health related charitable organisations in England, irrespective of their value to the company (standard information is available to all registered users). • The system only to be provided to a restricted number of named users, who have undergone and passed HES Protocol training underlining the need for non-commercial reuse All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established Prescription Medicines Code of Practice Authority (PMCPA) Code of Practice and Department of Health (DH) governance on the use of healthcare data by Life Science Companies with health and social care. Meditrends ensure that registered users are not using the data for solely commercial purposes by requiring each user to undertake a thorough online governance training and, for more bespoke requests, also apply particular scrutiny to the purpose for which the data is to be used. All data on Meditrends Online is aggregated data with small number suppression as described below in Processing Activities (Data Release). Purpose 2): Custom Analysis Meditrends Limited receives requests (as part of projects led by NHS organisations) for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on an ad-hoc basis and as part of longer term healthcare development projects. These requests may be limited to the provision of aggregated HES data or may require the provision of analysis and interpretation. The analyses Meditrends Limited undertakes are complicated and are required in rapid timeframes to achieve NHS and social care project objectives. In all cases, projects are led by NHS organisations or by other organisations in partnership with or in response to requests for information from NHS organisations or affiliated bodies, including NICE. In all cases, the data can only be used where the ultimate beneficiary is the NHS and Social Care. Therefore, as part of the contract, it is mandatory for 3rd parties to specify in advance their publication and communications plan to ensure that the results of the analysis are delivered in a non-promotional manner to the benefit of health and social care. It should be noted that Meditrends Ltd have turned down projects in the last year where the purpose was not felt to meet this requirement. The potential users of custom analyses are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care focussing on Academic Health Science Networks (AHSN) and NHS partners. 2. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Under purpose 2, 5 years data is required for benchmarking purposes. Where 10 years data may be required for particular projects, the applicant will submit an amendment to request that data for the project in question only. All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI). Since 2007 Meditrends Ltd has provided HES analyses to around 20 different organisations in the UK pharmaceutical and device industries. Typically there are four or five active projects at any particular time. All analyses outputs that use HES include small number suppression as described under Processing Activities (Data Release).	General Processing Activities: The general HES data processing cycle can be summarised as follows: HES Data Downloads and Storage: HES data is downloaded from the HSCIC via SEFT and transferred to a secure, fully encrypted, stand-alone server with no connection to the local network or internet. The HES data is imported to a relational database on this same machine for indexing and data cleaning. Access to this machine is restricted to Meditrends Limited staff who have undergone specific, documented and audited training in data governance. Data Processing: Processing of HES data for the production of aggregated outputs for Meditrends Online and custom analyses are only carried out on the stand-alone server. Aggregated outputs are then transferred to an encrypted, secure network for final processing and uploading for Meditrends Online. Meditrends match organisation level (aggregated) data from HES to publicly available GP Prescribing, Quality Outcomes Framework (QOF) and Organisation Data Service (ODS) data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Meditrends Limited to third parties. Data Release: All outputs are published at an aggregated level using HES data in line with the required guidelines and policy documentation. All outputs are subject to a two stage sign off process, involving a manual check of all tabulations. Specific Data Release Requirements Purpose 1): Meditrends Online The Meditrends website contains only aggregate HES data tables and is not hosted on the same network or otherwise connected to the episode level data. Before aggregated data is uploaded to the Meditrends Online website it is subject to a two stage offline and test environment sign-off process to confirm that small numbers have been suppressed as described above. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to Meditrends Online is via a secure password controlled web site. The access cycle for Meditrends Online is: • Each user organisation agrees a legal contract with Meditrends Limited stipulating terms and conditions (T&Cs). This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Meditrends Limited and the HSCIC o Restrictions on use of data outputs o Nomination of individual users within an organisation listing user names and job functions o Requirement to publish and reference (where possible) any work which uses the outputs of the HES within Meditrends Online o Confirmation that failure to apply with the above will result in Meditrends Limited removing the organisation from the approved user list and requiring the deletion of all data accessed through Meditrends Online • When the contract has been completed, Meditrends Limited provides HES Protocol training to all nominated users from an organisation, with an online assessment that demonstrates that users understand the regulations plus T&Cs relating to use of HES data outputs. • Individual Users provided with secure login details (username and password) that they must authenticate to access. • Users use Meditrends Online for the purposes defined in the T&Cs. • User login details to be active for restricted time before expiry and the reissue of new details. • Meditrends Limited is responsible for monitoring system usage, enforcing password rotation, and deleting inactive users. Purpose 2): Custom Analysis The outputs of Custom analyses are typically presentations, documents or tabulated data. In accordance with HES guidelines, these outputs only contain aggregated, small number suppressed data (as described above) and are subject to a documented internal sign-off process to confirm that this has been carried out. The access cycle for Custom Analyses is: 1. Contract signed with the 3rd party including: • What the analysis can and cannot be used for • The intended benefit to health & social care; and • The communication and publication plan for the analysis 2. Meditrends Limited undertake the analysis 3. Analysis outputs peer reviewed and quality assured, including checks for small number suppression by Meditrends Limited 4. Analysis released to 3rd party 5. Post release Meditrends Limited follow up adherence to communication and publication plan for analysis Purpose 1): Meditrends online Meditrends online is a complex reporting tool, delivering a broad range of metrics, which are based on pseudonymised episode level data. As an example, an important metric the site reports is a count of unique patient numbers as well as episode and spell counts, aggregated at various hierarchies (by geography, diagnosis and procedure). As patients can have multiple admissions covering several different points within the same hierarchy, when aggregating to higher levels, patient counts are not simply additive and therefore episode level data is needed with a pseudonymised patient identifier linked to each episode. Derived values in HES frequently become obsolete. For example: • Organisational structures change with re-organisations and trust mergers • HRGs definitions (based on underlying diagnostic and procedural codes) are changed annually Access to record level data allows HES data to be converted to present day NHS organisational structures and HRGs and maintains its usefulness. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Purpose 2): Custom Analysis The requirements for custom analyses are varied, but typically involve the following elements, all of which require episode level, pseudonymised data: • Unique patient counts • Aggregation of episodes into spells to enable accurate assessment of clinical burden and cost • Tracking patient cohorts over time • Epidemiological analysis of incidence, prevalence and co-morbidities Requirement for historic data Tracking patient cohorts requires data covering an extended time period, because studies are often trying to find precursors or predictors of particular conditions. For example in an MS cohort analysis described above, data was required covering a minimum 10 year period in order to allow a statistically significant cohort of patients with four years pre and post treatment history to be studied. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Projects frequently involve analysing subgroups of patients who have specific events in their history (e.g. previous myocardial infarction, implantation of a pacemaker). These events may have occurred a long time ago, so having access to as much historical data as possible improves the quality of the analyses. Epidemiological models produced by Meditrends often use the first admission with a particular diagnosis (e.g. multiple sclerosis) to estimate disease incidence. Having access to an extended dataset gives a more accurate view of long term trends which helps in producing forecasts of burden of disease. When looking at the impact of particular interventions or alternative treatment pathways there is often a need to follow a cohort for five years before and after a particular event (e.g. heart valve replacement). Only the minimum amount of filtered data will be used to conduct the required analysis at any one time. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Purpose 1): Meditrends Online Meditrends Online is a website that gives users access to a wide range of HES derived healthcare analytics though an intuitive user interface. These analytics will include hospital activity, disease epidemiology and health economic metrics, summarised at various geographical levels. The site went live as a pilot in April 2017 and has ~25 NHS users to date. 3 life science companies are also trialling the system, which equates to a maximum of 3 staff per company accessing the system. Purpose 2): Custom Analysis Custom analyses are produced as requested by 3rd parties. Outputs take the form of: • Interactive spreadsheet applications • Documents in Word or pdf format • PowerPoint presentations	Purpose 1): Meditrends Online Meditrends Online will allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The timelines for Meditrends Online are: • Meditrends Limited developed the database and website front end for Meditrends Online to an advanced stage, and was then in a position to launch the pilot in April 2017. • Meditrends Limited launched Meditrends Online in April 2017 • To date there are approximately 25 registered users of Meditrends Online based in NHS organisations. Meditrends Limited has a target of 100 unique NHS users by April 2018 i.e. a year after launch. 3 life science companies are also trialling the system, which equates to a maximum of 3 individuals per company accessing the system. Purpose 2): Custom Analysis Custom analyses are ad hoc in their nature and depend on which health & social care led topics Meditrends Limited is asked to respond to. Typically the applicant handles around 6 custom analyses at any one time due to capacity, as these are generally in-depth and resource intensive. An example of a custom analysis that Meditrends Limited anticipates undertaking if this application is approved is for a new medical device that will be introduced to treat heart valve defects in currently inoperable patients. Cardiology is the subject of direct commissioning by NHS England under programme A.09 (Complex Invasive Cardiology). As part of the process of evaluation for inclusion as a commissioned service, the client, will be required to provide to NHS England, evidence of the burden of the disease treated (in terms of hospital resource usage and cost), the size of the potential patient population and a segmentation of the potential population in terms of disease severity and risk to enable NHS England to determine the appropriate use of the technology. Meditrends Limited will use HES data provided under this application to help develop the evidence that NHS England will require. If this device is commissioned by NHS England providers and patients will benefit from having a new therapy option where none currently exists and the burden of medically managing these patients on the NHS will be ameliorated. The contracts between Meditrends Limited and 3rd parties for all custom analyses will document the anticipated benefit to health and social care and the communications and publication plan for delivering these benefits. To date the focus has been on getting the website fully operational, but there has also been some demand for custom analyses, with the findings due to be available over the next few months. Under the Objectives for Processing section above a number of projects were listed in 13 different clinical areas. These projects were carried out under Meditrends’ latest data-sharing agreement or earlier versions. It was the nature of these projects that clients would commission Meditrends to carry out analyses with the aims and objectives described under Objectives for Processing. Other than general feedback about the way the information was received, it would not have been part of the project engagement to monitor the ongoing use of the analyses with NHS organisations. In this application/agreement and in the agreement that this supersedes, Meditrends has committed to ensure that every project includes a publication plan, and clearly states the anticipated measurable benefits to health and social care. It is believed that their clients understand that the climate around HES data has changed, and they will be prepared to make the additional investment that this will entail. In future, therefore, Meditrends are confident that they will be able to provide the explicit evidence of benefits to support their ongoing access to pseudonymised HES data.
MEDITRENDS LTD	MEDITRENDS LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Meditrends Limited (incorporated in 2013, taking over the business of Beacon Consulting) will use the data solely for the following purposes (any other purposes will be subject to a further application):- Purpose 1): Meditrends Online Meditrends Online is a web delivered system that uses aggregated, supressed, non-sensitive, non-identifiable HES data to assist NHS commissioners throughout the commissioning cycle. Meditrends Online will allow users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The current users of Meditrends Online are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care including: a. NHS-GPs, Commissioners, Acute Trusts, Area & Regional Teams, Strategic Clinical networks; b. Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN); c. NHS England Commissioning Support Units (CSUs) 2. Patient support groups & other health related charitable organisations; 3. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology); All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI) Although the users of Meditrends Online are all of the above groups, the data can only be used for the purposes listed above, with the ultimate beneficiary in all cases being the NHS and Social Care. All users will give a written undertaking to this effect. Meditrends Online outputs will be used by the ultimate beneficiary to optimise the Commissioning Cycle in the following ways: • To assess performance against similar comparisons and to understand where change could be required to achieve Quality Innovation Productivity and Prevention (QIPP) planning. • To communicate with all stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. • To identify areas of best practice in disease management. To use this data to define, monitor and communicate critical indicators of success. • To forecast future levels of demand for services and configure local resources to meet these needs Life Science Companies are a user of the aggregated outputs exclusively for the purpose of using Meditrends Online to benefit the health and social care organisations in England. Life Science Companies users will be highly restricted in their use of Meditrends Online to ensure aggregated HES data are not used for commercial purposes such as targeting sales resource. These restrictions are underpinned through a binding legal contract which contains terms which require: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations or patient support groups & other health related charitable organisations in England • The system not to be used for solely commercial purposes • The same aggregated HES data outputs to be made available, if requested, to all health and social care organisations or patient support groups & other health related charitable organisations in England, irrespective of their value to the company (standard information is available to all registered users). • The system only to be provided to a restricted number of named users, who have undergone and passed HES Protocol training underlining the need for non-commercial reuse All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established Prescription Medicines Code of Practice Authority (PMCPA) Code of Practice and Department of Health (DH) governance on the use of healthcare data by Life Science Companies with health and social care. Meditrends ensure that registered users are not using the data for solely commercial purposes by requiring each user to undertake a thorough online governance training and, for more bespoke requests, also apply particular scrutiny to the purpose for which the data is to be used. All data on Meditrends Online is aggregated data with small number suppression as described below in Processing Activities (Data Release). Purpose 2): Custom Analysis Meditrends Limited receives requests (as part of projects led by NHS organisations) for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on an ad-hoc basis and as part of longer term healthcare development projects. These requests may be limited to the provision of aggregated HES data or may require the provision of analysis and interpretation. The analyses Meditrends Limited undertakes are complicated and are required in rapid timeframes to achieve NHS and social care project objectives. In all cases, projects are led by NHS organisations or by other organisations in partnership with or in response to requests for information from NHS organisations or affiliated bodies, including NICE. In all cases, the data can only be used where the ultimate beneficiary is the NHS and Social Care. Therefore, as part of the contract, it is mandatory for 3rd parties to specify in advance their publication and communications plan to ensure that the results of the analysis are delivered in a non-promotional manner to the benefit of health and social care. It should be noted that Meditrends Ltd have turned down projects in the last year where the purpose was not felt to meet this requirement. The potential users of custom analyses are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care focussing on Academic Health Science Networks (AHSN) and NHS partners. 2. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Under purpose 2, 5 years data is required for benchmarking purposes. Where 10 years data may be required for particular projects, the applicant will submit an amendment to request that data for the project in question only. All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI). Since 2007 Meditrends Ltd has provided HES analyses to around 20 different organisations in the UK pharmaceutical and device industries. Typically there are four or five active projects at any particular time. All analyses outputs that use HES include small number suppression as described under Processing Activities (Data Release).	General Processing Activities: The general HES data processing cycle can be summarised as follows: HES Data Downloads and Storage: HES data is downloaded from the HSCIC via SEFT and transferred to a secure, fully encrypted, stand-alone server with no connection to the local network or internet. The HES data is imported to a relational database on this same machine for indexing and data cleaning. Access to this machine is restricted to Meditrends Limited staff who have undergone specific, documented and audited training in data governance. Data Processing: Processing of HES data for the production of aggregated outputs for Meditrends Online and custom analyses are only carried out on the stand-alone server. Aggregated outputs are then transferred to an encrypted, secure network for final processing and uploading for Meditrends Online. Meditrends match organisation level (aggregated) data from HES to publicly available GP Prescribing, Quality Outcomes Framework (QOF) and Organisation Data Service (ODS) data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Meditrends Limited to third parties. Data Release: All outputs are published at an aggregated level using HES data in line with the required guidelines and policy documentation. All outputs are subject to a two stage sign off process, involving a manual check of all tabulations. Specific Data Release Requirements Purpose 1): Meditrends Online The Meditrends website contains only aggregate HES data tables and is not hosted on the same network or otherwise connected to the episode level data. Before aggregated data is uploaded to the Meditrends Online website it is subject to a two stage offline and test environment sign-off process to confirm that small numbers have been suppressed as described above. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to Meditrends Online is via a secure password controlled web site. The access cycle for Meditrends Online is: • Each user organisation agrees a legal contract with Meditrends Limited stipulating terms and conditions (T&Cs). This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Meditrends Limited and the HSCIC o Restrictions on use of data outputs o Nomination of individual users within an organisation listing user names and job functions o Requirement to publish and reference (where possible) any work which uses the outputs of the HES within Meditrends Online o Confirmation that failure to apply with the above will result in Meditrends Limited removing the organisation from the approved user list and requiring the deletion of all data accessed through Meditrends Online • When the contract has been completed, Meditrends Limited provides HES Protocol training to all nominated users from an organisation, with an online assessment that demonstrates that users understand the regulations plus T&Cs relating to use of HES data outputs. • Individual Users provided with secure login details (username and password) that they must authenticate to access. • Users use Meditrends Online for the purposes defined in the T&Cs. • User login details to be active for restricted time before expiry and the reissue of new details. • Meditrends Limited is responsible for monitoring system usage, enforcing password rotation, and deleting inactive users. Purpose 2): Custom Analysis The outputs of Custom analyses are typically presentations, documents or tabulated data. In accordance with HES guidelines, these outputs only contain aggregated, small number suppressed data (as described above) and are subject to a documented internal sign-off process to confirm that this has been carried out. The access cycle for Custom Analyses is: 1. Contract signed with the 3rd party including: • What the analysis can and cannot be used for • The intended benefit to health & social care; and • The communication and publication plan for the analysis 2. Meditrends Limited undertake the analysis 3. Analysis outputs peer reviewed and quality assured, including checks for small number suppression by Meditrends Limited 4. Analysis released to 3rd party 5. Post release Meditrends Limited follow up adherence to communication and publication plan for analysis Purpose 1): Meditrends online Meditrends online is a complex reporting tool, delivering a broad range of metrics, which are based on pseudonymised episode level data. As an example, an important metric the site reports is a count of unique patient numbers as well as episode and spell counts, aggregated at various hierarchies (by geography, diagnosis and procedure). As patients can have multiple admissions covering several different points within the same hierarchy, when aggregating to higher levels, patient counts are not simply additive and therefore episode level data is needed with a pseudonymised patient identifier linked to each episode. Derived values in HES frequently become obsolete. For example: • Organisational structures change with re-organisations and trust mergers • HRGs definitions (based on underlying diagnostic and procedural codes) are changed annually Access to record level data allows HES data to be converted to present day NHS organisational structures and HRGs and maintains its usefulness. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Purpose 2): Custom Analysis The requirements for custom analyses are varied, but typically involve the following elements, all of which require episode level, pseudonymised data: • Unique patient counts • Aggregation of episodes into spells to enable accurate assessment of clinical burden and cost • Tracking patient cohorts over time • Epidemiological analysis of incidence, prevalence and co-morbidities Requirement for historic data Tracking patient cohorts requires data covering an extended time period, because studies are often trying to find precursors or predictors of particular conditions. For example in an MS cohort analysis described above, data was required covering a minimum 10 year period in order to allow a statistically significant cohort of patients with four years pre and post treatment history to be studied. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Projects frequently involve analysing subgroups of patients who have specific events in their history (e.g. previous myocardial infarction, implantation of a pacemaker). These events may have occurred a long time ago, so having access to as much historical data as possible improves the quality of the analyses. Epidemiological models produced by Meditrends often use the first admission with a particular diagnosis (e.g. multiple sclerosis) to estimate disease incidence. Having access to an extended dataset gives a more accurate view of long term trends which helps in producing forecasts of burden of disease. When looking at the impact of particular interventions or alternative treatment pathways there is often a need to follow a cohort for five years before and after a particular event (e.g. heart valve replacement). Only the minimum amount of filtered data will be used to conduct the required analysis at any one time. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Purpose 1): Meditrends Online Meditrends Online is a website that gives users access to a wide range of HES derived healthcare analytics though an intuitive user interface. These analytics will include hospital activity, disease epidemiology and health economic metrics, summarised at various geographical levels. The site went live as a pilot in April 2017 and has ~25 NHS users to date. 3 life science companies are also trialling the system, which equates to a maximum of 3 staff per company accessing the system. Purpose 2): Custom Analysis Custom analyses are produced as requested by 3rd parties. Outputs take the form of: • Interactive spreadsheet applications • Documents in Word or pdf format • PowerPoint presentations	Purpose 1): Meditrends Online Meditrends Online will allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The timelines for Meditrends Online are: • Meditrends Limited developed the database and website front end for Meditrends Online to an advanced stage, and was then in a position to launch the pilot in April 2017. • Meditrends Limited launched Meditrends Online in April 2017 • To date there are approximately 25 registered users of Meditrends Online based in NHS organisations. Meditrends Limited has a target of 100 unique NHS users by April 2018 i.e. a year after launch. 3 life science companies are also trialling the system, which equates to a maximum of 3 individuals per company accessing the system. Purpose 2): Custom Analysis Custom analyses are ad hoc in their nature and depend on which health & social care led topics Meditrends Limited is asked to respond to. Typically the applicant handles around 6 custom analyses at any one time due to capacity, as these are generally in-depth and resource intensive. An example of a custom analysis that Meditrends Limited anticipates undertaking if this application is approved is for a new medical device that will be introduced to treat heart valve defects in currently inoperable patients. Cardiology is the subject of direct commissioning by NHS England under programme A.09 (Complex Invasive Cardiology). As part of the process of evaluation for inclusion as a commissioned service, the client, will be required to provide to NHS England, evidence of the burden of the disease treated (in terms of hospital resource usage and cost), the size of the potential patient population and a segmentation of the potential population in terms of disease severity and risk to enable NHS England to determine the appropriate use of the technology. Meditrends Limited will use HES data provided under this application to help develop the evidence that NHS England will require. If this device is commissioned by NHS England providers and patients will benefit from having a new therapy option where none currently exists and the burden of medically managing these patients on the NHS will be ameliorated. The contracts between Meditrends Limited and 3rd parties for all custom analyses will document the anticipated benefit to health and social care and the communications and publication plan for delivering these benefits. To date the focus has been on getting the website fully operational, but there has also been some demand for custom analyses, with the findings due to be available over the next few months. Under the Objectives for Processing section above a number of projects were listed in 13 different clinical areas. These projects were carried out under Meditrends’ latest data-sharing agreement or earlier versions. It was the nature of these projects that clients would commission Meditrends to carry out analyses with the aims and objectives described under Objectives for Processing. Other than general feedback about the way the information was received, it would not have been part of the project engagement to monitor the ongoing use of the analyses with NHS organisations. In this application/agreement and in the agreement that this supersedes, Meditrends has committed to ensure that every project includes a publication plan, and clearly states the anticipated measurable benefits to health and social care. It is believed that their clients understand that the climate around HES data has changed, and they will be prepared to make the additional investment that this will entail. In future, therefore, Meditrends are confident that they will be able to provide the explicit evidence of benefits to support their ongoing access to pseudonymised HES data.
MEDITRENDS LTD	MEDITRENDS LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Meditrends Limited (incorporated in 2013, taking over the business of Beacon Consulting) will use the data solely for the following purposes (any other purposes will be subject to a further application):- Purpose 1): Meditrends Online Meditrends Online is a web delivered system that uses aggregated, supressed, non-sensitive, non-identifiable HES data to assist NHS commissioners throughout the commissioning cycle. Meditrends Online will allow users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The current users of Meditrends Online are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care including: a. NHS-GPs, Commissioners, Acute Trusts, Area & Regional Teams, Strategic Clinical networks; b. Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN); c. NHS England Commissioning Support Units (CSUs) 2. Patient support groups & other health related charitable organisations; 3. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology); All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI) Although the users of Meditrends Online are all of the above groups, the data can only be used for the purposes listed above, with the ultimate beneficiary in all cases being the NHS and Social Care. All users will give a written undertaking to this effect. Meditrends Online outputs will be used by the ultimate beneficiary to optimise the Commissioning Cycle in the following ways: • To assess performance against similar comparisons and to understand where change could be required to achieve Quality Innovation Productivity and Prevention (QIPP) planning. • To communicate with all stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. • To identify areas of best practice in disease management. To use this data to define, monitor and communicate critical indicators of success. • To forecast future levels of demand for services and configure local resources to meet these needs Life Science Companies are a user of the aggregated outputs exclusively for the purpose of using Meditrends Online to benefit the health and social care organisations in England. Life Science Companies users will be highly restricted in their use of Meditrends Online to ensure aggregated HES data are not used for commercial purposes such as targeting sales resource. These restrictions are underpinned through a binding legal contract which contains terms which require: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations or patient support groups & other health related charitable organisations in England • The system not to be used for solely commercial purposes • The same aggregated HES data outputs to be made available, if requested, to all health and social care organisations or patient support groups & other health related charitable organisations in England, irrespective of their value to the company (standard information is available to all registered users). • The system only to be provided to a restricted number of named users, who have undergone and passed HES Protocol training underlining the need for non-commercial reuse All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established Prescription Medicines Code of Practice Authority (PMCPA) Code of Practice and Department of Health (DH) governance on the use of healthcare data by Life Science Companies with health and social care. Meditrends ensure that registered users are not using the data for solely commercial purposes by requiring each user to undertake a thorough online governance training and, for more bespoke requests, also apply particular scrutiny to the purpose for which the data is to be used. All data on Meditrends Online is aggregated data with small number suppression as described below in Processing Activities (Data Release). Purpose 2): Custom Analysis Meditrends Limited receives requests (as part of projects led by NHS organisations) for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on an ad-hoc basis and as part of longer term healthcare development projects. These requests may be limited to the provision of aggregated HES data or may require the provision of analysis and interpretation. The analyses Meditrends Limited undertakes are complicated and are required in rapid timeframes to achieve NHS and social care project objectives. In all cases, projects are led by NHS organisations or by other organisations in partnership with or in response to requests for information from NHS organisations or affiliated bodies, including NICE. In all cases, the data can only be used where the ultimate beneficiary is the NHS and Social Care. Therefore, as part of the contract, it is mandatory for 3rd parties to specify in advance their publication and communications plan to ensure that the results of the analysis are delivered in a non-promotional manner to the benefit of health and social care. It should be noted that Meditrends Ltd have turned down projects in the last year where the purpose was not felt to meet this requirement. The potential users of custom analyses are: 1. Public Sector Organisations responsible for the planning, evaluation, commissioning or provision of health and social care focussing on Academic Health Science Networks (AHSN) and NHS partners. 2. Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Under purpose 2, 5 years data is required for benchmarking purposes. Where 10 years data may be required for particular projects, the applicant will submit an amendment to request that data for the project in question only. All Life Science Companies are required to be members of the Association of British Pharmaceutical Companies (ABPI); the UK BioIndustry Association or the Association of British Healthcare Industries (ABHI). Since 2007 Meditrends Ltd has provided HES analyses to around 20 different organisations in the UK pharmaceutical and device industries. Typically there are four or five active projects at any particular time. All analyses outputs that use HES include small number suppression as described under Processing Activities (Data Release).	General Processing Activities: The general HES data processing cycle can be summarised as follows: HES Data Downloads and Storage: HES data is downloaded from the HSCIC via SEFT and transferred to a secure, fully encrypted, stand-alone server with no connection to the local network or internet. The HES data is imported to a relational database on this same machine for indexing and data cleaning. Access to this machine is restricted to Meditrends Limited staff who have undergone specific, documented and audited training in data governance. Data Processing: Processing of HES data for the production of aggregated outputs for Meditrends Online and custom analyses are only carried out on the stand-alone server. Aggregated outputs are then transferred to an encrypted, secure network for final processing and uploading for Meditrends Online. Meditrends match organisation level (aggregated) data from HES to publicly available GP Prescribing, Quality Outcomes Framework (QOF) and Organisation Data Service (ODS) data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Meditrends Limited to third parties. Data Release: All outputs are published at an aggregated level using HES data in line with the required guidelines and policy documentation. All outputs are subject to a two stage sign off process, involving a manual check of all tabulations. Specific Data Release Requirements Purpose 1): Meditrends Online The Meditrends website contains only aggregate HES data tables and is not hosted on the same network or otherwise connected to the episode level data. Before aggregated data is uploaded to the Meditrends Online website it is subject to a two stage offline and test environment sign-off process to confirm that small numbers have been suppressed as described above. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to Meditrends Online is via a secure password controlled web site. The access cycle for Meditrends Online is: • Each user organisation agrees a legal contract with Meditrends Limited stipulating terms and conditions (T&Cs). This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Meditrends Limited and the HSCIC o Restrictions on use of data outputs o Nomination of individual users within an organisation listing user names and job functions o Requirement to publish and reference (where possible) any work which uses the outputs of the HES within Meditrends Online o Confirmation that failure to apply with the above will result in Meditrends Limited removing the organisation from the approved user list and requiring the deletion of all data accessed through Meditrends Online • When the contract has been completed, Meditrends Limited provides HES Protocol training to all nominated users from an organisation, with an online assessment that demonstrates that users understand the regulations plus T&Cs relating to use of HES data outputs. • Individual Users provided with secure login details (username and password) that they must authenticate to access. • Users use Meditrends Online for the purposes defined in the T&Cs. • User login details to be active for restricted time before expiry and the reissue of new details. • Meditrends Limited is responsible for monitoring system usage, enforcing password rotation, and deleting inactive users. Purpose 2): Custom Analysis The outputs of Custom analyses are typically presentations, documents or tabulated data. In accordance with HES guidelines, these outputs only contain aggregated, small number suppressed data (as described above) and are subject to a documented internal sign-off process to confirm that this has been carried out. The access cycle for Custom Analyses is: 1. Contract signed with the 3rd party including: • What the analysis can and cannot be used for • The intended benefit to health & social care; and • The communication and publication plan for the analysis 2. Meditrends Limited undertake the analysis 3. Analysis outputs peer reviewed and quality assured, including checks for small number suppression by Meditrends Limited 4. Analysis released to 3rd party 5. Post release Meditrends Limited follow up adherence to communication and publication plan for analysis Purpose 1): Meditrends online Meditrends online is a complex reporting tool, delivering a broad range of metrics, which are based on pseudonymised episode level data. As an example, an important metric the site reports is a count of unique patient numbers as well as episode and spell counts, aggregated at various hierarchies (by geography, diagnosis and procedure). As patients can have multiple admissions covering several different points within the same hierarchy, when aggregating to higher levels, patient counts are not simply additive and therefore episode level data is needed with a pseudonymised patient identifier linked to each episode. Derived values in HES frequently become obsolete. For example: • Organisational structures change with re-organisations and trust mergers • HRGs definitions (based on underlying diagnostic and procedural codes) are changed annually Access to record level data allows HES data to be converted to present day NHS organisational structures and HRGs and maintains its usefulness. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Purpose 2): Custom Analysis The requirements for custom analyses are varied, but typically involve the following elements, all of which require episode level, pseudonymised data: • Unique patient counts • Aggregation of episodes into spells to enable accurate assessment of clinical burden and cost • Tracking patient cohorts over time • Epidemiological analysis of incidence, prevalence and co-morbidities Requirement for historic data Tracking patient cohorts requires data covering an extended time period, because studies are often trying to find precursors or predictors of particular conditions. For example in an MS cohort analysis described above, data was required covering a minimum 10 year period in order to allow a statistically significant cohort of patients with four years pre and post treatment history to be studied. Many conditions are chronic or slow to progress and the ability to study cohorts over 10 years after the first diagnosis gives valuable insights to health and social care, for example in identifying areas of success or those where there is an opportunity for improvement. Projects frequently involve analysing subgroups of patients who have specific events in their history (e.g. previous myocardial infarction, implantation of a pacemaker). These events may have occurred a long time ago, so having access to as much historical data as possible improves the quality of the analyses. Epidemiological models produced by Meditrends often use the first admission with a particular diagnosis (e.g. multiple sclerosis) to estimate disease incidence. Having access to an extended dataset gives a more accurate view of long term trends which helps in producing forecasts of burden of disease. When looking at the impact of particular interventions or alternative treatment pathways there is often a need to follow a cohort for five years before and after a particular event (e.g. heart valve replacement). Only the minimum amount of filtered data will be used to conduct the required analysis at any one time. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Purpose 1): Meditrends Online Meditrends Online is a website that gives users access to a wide range of HES derived healthcare analytics though an intuitive user interface. These analytics will include hospital activity, disease epidemiology and health economic metrics, summarised at various geographical levels. The site went live as a pilot in April 2017 and has ~25 NHS users to date. 3 life science companies are also trialling the system, which equates to a maximum of 3 staff per company accessing the system. Purpose 2): Custom Analysis Custom analyses are produced as requested by 3rd parties. Outputs take the form of: • Interactive spreadsheet applications • Documents in Word or pdf format • PowerPoint presentations	Purpose 1): Meditrends Online Meditrends Online will allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of newly implemented pathways and services. The timelines for Meditrends Online are: • Meditrends Limited developed the database and website front end for Meditrends Online to an advanced stage, and was then in a position to launch the pilot in April 2017. • Meditrends Limited launched Meditrends Online in April 2017 • To date there are approximately 25 registered users of Meditrends Online based in NHS organisations. Meditrends Limited has a target of 100 unique NHS users by April 2018 i.e. a year after launch. 3 life science companies are also trialling the system, which equates to a maximum of 3 individuals per company accessing the system. Purpose 2): Custom Analysis Custom analyses are ad hoc in their nature and depend on which health & social care led topics Meditrends Limited is asked to respond to. Typically the applicant handles around 6 custom analyses at any one time due to capacity, as these are generally in-depth and resource intensive. An example of a custom analysis that Meditrends Limited anticipates undertaking if this application is approved is for a new medical device that will be introduced to treat heart valve defects in currently inoperable patients. Cardiology is the subject of direct commissioning by NHS England under programme A.09 (Complex Invasive Cardiology). As part of the process of evaluation for inclusion as a commissioned service, the client, will be required to provide to NHS England, evidence of the burden of the disease treated (in terms of hospital resource usage and cost), the size of the potential patient population and a segmentation of the potential population in terms of disease severity and risk to enable NHS England to determine the appropriate use of the technology. Meditrends Limited will use HES data provided under this application to help develop the evidence that NHS England will require. If this device is commissioned by NHS England providers and patients will benefit from having a new therapy option where none currently exists and the burden of medically managing these patients on the NHS will be ameliorated. The contracts between Meditrends Limited and 3rd parties for all custom analyses will document the anticipated benefit to health and social care and the communications and publication plan for delivering these benefits. To date the focus has been on getting the website fully operational, but there has also been some demand for custom analyses, with the findings due to be available over the next few months. Under the Objectives for Processing section above a number of projects were listed in 13 different clinical areas. These projects were carried out under Meditrends’ latest data-sharing agreement or earlier versions. It was the nature of these projects that clients would commission Meditrends to carry out analyses with the aims and objectives described under Objectives for Processing. Other than general feedback about the way the information was received, it would not have been part of the project engagement to monitor the ongoing use of the analyses with NHS organisations. In this application/agreement and in the agreement that this supersedes, Meditrends has committed to ensure that every project includes a publication plan, and clearly states the anticipated measurable benefits to health and social care. It is believed that their clients understand that the climate around HES data has changed, and they will be prepared to make the additional investment that this will entail. In future, therefore, Meditrends are confident that they will be able to provide the explicit evidence of benefits to support their ongoing access to pseudonymised HES data.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	No ONS data will be released under this agreement. The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by DAAG the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCICs secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator, as per the HSCIC guidance who has the password for the secure ftp.. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric environment, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at RedCentric. The development of Qlikview tools will be undertaken in the secure Redcentric environment. 1) HSCIC and ONS Data is processed into indicators within the Redcentric environment and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in Redcentric and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc) before it is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric environment and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within Redcentric over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section 5a.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to 'drill down' to indovidual patient cohorts, will enahce users ability to understand and identify potential improvements in care to reduce mortality. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Standard Monthly Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Standard Monthly Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Standard Monthly Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
METHODS ANALYTICS LTD	METHODS ANALYTICS LTD	Standard Monthly Extract : SUS PbR OP	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to support the NHS either directly through the delivery of tools and bespoke analysis or indirectly through non-NHS organisations, where analytics are provided to the NHS as the end beneficiary via a non-NHS organisation. The organisations who would be considered as supporting the NHS directly are: Department of Health Monitor NHS Trust Development Agency NHS Improvement NHS England NICE CCGs CSUs Local Authorities (for Public Health purposes only) providers of NHS-funded care professional bodies This list is referred to as “Healthcare Organisations” below. Organisations who would be considered as supporting the NHS indirectly only where they are providing analysis/analytics to one of the “Healthcare Organisations”. Such organisations work within the healthcare space and have access to analysis solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. Methods Analytics' target audience is NHS organisations, however the NHS is increasingly looking to industry to support it in the provision of evidence and implementation support for service improvement, and hence Methods Analytics wish to offer the tool services to a limited number of non-nhs organisations based on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, the schedule will state that: • Only aggregated small number suppressed data may be used from the tool. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities • No ONS data or derivatives of ONS data will be provided to non-NHS organisations There are five uses of data requested (and each is discussed further within the processing, outputs and benefits section). The specific uses are :- 1) For Stethoscope - a quality variation tool which provides national benchmarking of HES based indicators that is made available free to the public at an organisation roll up level, and more granular information to subscribing Healthcare Organisations, as listed above, and non-NHS organisations undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and have access to the system solely for the purpose of assisting NHS organisations. Such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide on their agreeing to license terms and conditions, which include submitting and evidencing training in information governance and the restriction of the use of the tool to the uses outlined in this document, with this purpose statement flowed down as a contract schedule. For clarity, this will state that: • No record level data is provided to any third party organisation in any format. • No data is to be used for direct marketing to individuals or organisations. • No data is to be used for direct sales activities. Non-NHS organisations to be included are: Healthcare Organisations, charity and not-for-profit organisations , academic researchers, companies that specialise in providing commissioning support and service improvement services to the NHS and life science companies. No other non-NHS organisations are permitted. Consultant code will also provide a further level of drilldown in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more granular tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. 2) For bespoke tools and analysis for individual NHS clients (Healthcare Organisations, as listed above) and non-NHS organisations (as listed above) undertaking service improvement support for NHS benefit. Such organisations work within the healthcare space and receive analysis solely for the purpose of NHS benefit. All such organisations will only be provided with aggregate, small number suppressed data in line with the HES Analysis Guide. The majority of these reports contain data items from Stethoscope but are reported as dashboards for individual organisations. They also contain bespoke metrics generated from HES data presented as aggregated (small number suppressed in line with HES Analysis Guide) tabulated data and/or charts and graphics, and can have accompanying narrative interpretation. Methods Analytics may choose to place tabulations in the public domain (via Methods website or partner website) where a tabulation has been produced to support academic work or for other analysis under the terms of this agreement where there is public benefit in be a provider of open data. All such tabulations will be aggregate, small number suppressed in line with the HES analysis guide. 3) For creating and hosting dashboards and an explorer tool developed with the surgical associations working group under a NICE accredited methodology. This is work for the National Surgical Commissioning Centre, hosted by the Royal College of Surgeons of England and part of the NHS England Rightcare programme. These tools show activity rates and simple outcomes for CCG populations and care providers using HES/SUS PbR data. These tools are free to the public. 4) SWORD is a project for a number of the specialist surgical societies (which are registered charities) to develop an intelligence tool for only their Consultant Surgeons members to access measures and metrics about their own performance, which will be accessible via the associations’ member’s portals (therefore password protected). Only consultant surgeon members of the associations can access the SWORD tool. Access is further restricted so that surgeons can only access pathways developed with and for their specialist association and not those pertaining to other specialties. This is further secured by the request for access being generated by the association and sent to Methods Analytics, with Consultant name, GMC number and nhs.net email address that is used for communication with the individual. Method Analytics creates an account for that consultant with access granted only to pathways developed with and for the requesting association. When the user logs in the system validates a link between their user name and GMC number, so when they click the ‘consultant view’ they see only their own data with a national mean. At this level only data for the named consultant is visible. As requested and previously approved by NHS Digital the surgical associations individual consultants may see their own activity and outcomes without suppression, and national mean data to enable local discussion amongst surgeons of low volume activity and outcomes. There is no option to view other consultants’ data in this view. If the user does not have a valid GMC number linked to their user account, then when a user clicks on consultant view no information is presented. The other use case for SWORD is ‘pathway view’ where a user looks at an organisation level comparative (benchmarking) data for an individual surgical pathway, such as cholecystectomy, groin hernia etc with the ability to drill in and investigate how behaviour varies for groups of patients (grouped by a common theme eg: treatment pathway, not by identifiers). The surgical associations have now requested that Methods Analytics do not undertake suppression in this view either as to do so compromises the quality and accuracy of data, meaning too much data is missing to form a complete and accurate picture of what is going on clinically for patients on these pathways and significantly reduces the value of tool to the surgical community. The ability to look at sub-cohorts of activity and understand variation in decision making and low volume activity is a core use case, as stated the entire tool is only available to active consultant surgeons and they can only view pathways developed with and for their specialty association. The pathway view without suppression is deemed vital for clinical engagement, improvement of data quality and improvement in surgical decision making and patient outcomes by providing insight into clinical behaviours that it would be desirable to understand and potentially challenge, and identify if there are places in the country that are doing well and can peer support improvement in these pathways for those struggling. All users of the SWORD tool, where unsuppressed small numbers data may be visible, are required to sign terms of use which state that they must not :- - Onwardly share any data with small numbers within ; - Seek to re-identify any individual from that data. 5) The HSCIC developed the Summary Hospital Mortality Indicator (SHMI) and provides quarterly publications for each Trust in England. This includes an observed number of deaths within that period that occurred in hospital plus the number of deaths which occurred within 30 days of discharge from hospital. Using the HES-ONS linked dataset held previously, Methods Analytics were able to reproduce the exact methodology and figures in a timely manner which will allow subscribing NHS medical directors, chief executives, clinicians and managers to explore how the SHMI has changed over time and how their own trust is performing against other trusts in the country in terms of mortality rates. This means the data can be used to identify any issues and to improve the quality of care and to reduce patient mortality. ONS data will be used to create SHMI , variants of SHMI and other mortality metrics and include it as content in items 1-4 above as indicators. No ONS data or derivatives of ONS data will be provided to non-NHS organisations. Methods Analytics are able, at this time, to undertake the analysis of their commissioners require without needing ‘identifiable’ date of death. This request is for the provision of the non-identifiable variant including month and year of death and 30/60/90/180 and 365 day flags as is routinely provided. Methods Analytics require six years of data as, for their NHS users, they produce a number of variants of the national SHMI metric. The National methodology for creating this requires a three year historical data set to create the statistical model that derives SHMI. Methods Analytics surface to users in their tools a three year view (36 monthly periods to date). Therefore at entry to a new financial year, Methods Analytics require the three years they show to NHS users and the three years prior to that to create the statistical model, (totalling a 6 year data set). Methods Analytics then need to maintain this and add year to date, applying the model, month on month. At the end of the financial year Methods Analytics destroy the oldest years’ data once they have received M13 of the most recent year. Methods Analytics require MHLDDS, DIDs and bridging files in response to requests from current NHS commissioner and provider clients of their services. There is significant interest from both commissioners and providers of NHS care to understand the complete end-to-end care pathway, informing users of ‘whole system’ behaviour. This is a strong current NHS focus, including cross-care sector integration. With this data set Methods Analytics will be able to create information on different pathway models, such as direct access, traditional pathways via an acute outpatient appointment or the impact of referral management and triage to diagnostic designs. These data can inform an understanding of variation in total pathway duration, number of contacts required and total cost. This will enable the realisation of direct benefits to both commissioners and providers through improved evidence for design, monitoring and managing more efficient and effective care pathways, increasing value to both the NHS and patients. The new data sets will be used across purposes 1- 4 above.	For all purposes above the data is made available to Methods Analytics through HSCIC's secure ftp, after which it is imported into directly into Methods Analytics SQL data warehouse that is hosted as discrete physical servers by Redcentric. The process is handled by a single Data Base Administrator (as per the HSCIC guidance), who has the password for the secure ftp. This process means the individual will set off a set of automatic instructions to import the data into SQL via an SSIS package. The package itself handles the data import process. Redcentric provide rack space, power, internet connectivity (controlled by physical firewalls) and high level server management support (such as server system software patching). They do not have any access to data within the servers. Methods Analytics will install, maintain and operate all non-operating system software and have sole access to the servers. The installed software will be MS SQL Server 2014, and R server statistics package. Methods Analytics users will have to complete a Data Centre Access request form which is signed off by their line manager before being granted access the server. Using 2 factor authentication, encrypted, VPN. The VPN supports the use of both SafeNet Software based tokens and hardware based tokens each of these types requires a pin-code in order to generate a one-time password for the VPN. Each token is only usable on one device. The use of tokens restricts each Methods Analytics user to a single computer, with a drive encrypted using Microsoft Bitlocker. With the unique token issued to each member of Methods Analytics staff who need access, this identifies them discretely and incorporates user level access control limiting access to tables and data at a per user level. Data on the servers is encrypted using XTS AES 256-bit encryption at rest. All processing will be undertaken within the server environment. No episode level data will leave the secure environment. Once the record level data has been processed, only anonymous, aggregated data (with small numbers suppressed in line with the HES analysis guide), is transferred out of the secure Redcentric data centre, as described in individual sections below. No record level data is provided to any third party, and all record level or aggregated data (small numbers unsuppressed data) is held at the RedCentric data centre. The development of Qlikview tools will be undertaken in the secure Redcentric data centre. 1) HSCIC and ONS Data is processed into indicators within the Redcentric data centre and the resulting aggregated data (with small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure internal unidirectional VPN tunnel. Suppression of small numbers as per the HES Analytics Guide is applied by the Qlikview product as the application is used, and thus ensures that no small number unsuppressed data is available to the user. Stethoscope’s functionality built in the web for providing indicator Alerts and MyView uses a different data model to Qlikview. The data is still processed in the Redcentric data centre and suppressed in line with HES Analysis Guide. Whilst the Analysis Guide does permit small numbers at certain geographical levels, Methods Analytics apply small number suppression to any low numbers in the data table regardless of the level of aggregation (i.e. Regional, Provider etc). Aggregated data with small numbers suppressed (in line with the HES Analysis Guide) is transferred to an Amazon Web Service instance (in the EU Ireland region), where the data is restored to a SQL database which serves the web product. To be clear, only aggregated small number suppressed data (anonymous data therefore) is held or processed within Ireland. Record-level data or aggregate data containing small numbers, is not permitted to be transferred to the Amazon Web Service instance in Ireland. 2). HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and undergo a process to create an anonymous, small number suppressed tabulation. Those tabulations, which are suppressed in line with the HES Analysis Guide, are transferred via encrypted VPN to encrypted PCs/laptops in order to build reports using a suite of business intelligence software consisting of MS Office, MS PowerBI, Qlikview or Tableau. Reports are also rendered as Adobe PDF documents before being distributed. 3) HSCIC and ONS Data is processed into indicators and counts within the Redcentric data centre and the resulting aggregated (small numbers unsuppressed) dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that small numbers are not available to the user. 4) HSCIC and ONS Data is processed into indicators and counts within the server environment and the resulting pseudonymised dataset is transferred to a Qlikview server which is also within the Redcentric data centre over a secure unidirectional VPN tunnel. Suppression of small numbers as per the HES Analysis Guide is applied at the application layer (Qlikview) and ensures that no numbers <=5 are available to the user at this time, except where expressly permitted above in section "Objective for Processing". Use of ONS data is subject to ONS terms and conditions.	Outputs for the data will be as follows and are related back to the above objectives. All outputs are still live and on-going. 1) Stethoscope. Tool developed as Ruby on Rails bespoke web tool combined with Qlikview 11 dashboards https://stethoscope.methods.co.uk. 2) Ad hoc reports and bespoke tools ongoing. Created using MS Office Suite , Excel 2013, Word 2013, Qlikview and Tableau and also rendered as .PDF format for final reports on highly aggregate data. These include multiple reports on Mortality and Emergency care are for CCGs, providers and NHS England regions to support understanding of causes of failure and direct service improvement initiatives. Methods are also supporting the DH GIRFT programme, a wide ranging programme to improve secondary care quality and outcomes, with bespoke analysis and reporting for every provider in England. 3) Royal College of Surgeons Live dashboards and tool developed in Qlikview11 called the Procedures Explorer Tool (http://rcs.methods.co.uk/pet.html) and have recently produced surgical deep dive reports for every CCG and provider in England. 4) The output is an application, SWORD, at https://sword.methods.co.uk	Benefits relating to each of the purpose statements is listed below: 1) Stethoscope Free (formerly Acute Trust Quality Dashboard) free to the NHS and the public is information tool showing aggregated indicator data across the domains of the NHS operating framework. This has significant usage across the NHS with hundreds of visits each period and users can download a free pdf report, with approximately 7000 views and 30 free pdfs downloaded by users each month. The free public Stethoscope website was used as input for the Keogh mortality reviews and is visited by Monitor, CQC and NTDA among many others. Methods are aware that the free pdf download is used to inform Trust boards, having been asked for permission by Trust secretaries. Stethoscope Subscriber a password protected secure service offered with an annual subscription to cover the costs of data hosting and processing, licensing for Qlikview, development of the tool and hosting user groups. This offers users much more frequently than publicly available sources updated indicator data with the ability to drill into the data and filter by different options to provide insight and understanding of the quality of care. Users would be assigned access to the tool by an administrator in their organisation and examples of users include Trust Chief Executives, Medical and Nursing Directors, Specialty Managers, Clinicians and Information Departments. CCG, Local Area Team and commissioning region subscribers may grant access to the tool for use by Quality Managers, Public Health analysts, Commissioning Managers and Executives. It is important for Methods Analytics to work with their customers to ensure they can interpret the data and use it to take appropriate actions to safeguard against excess mortality and reduce mortality and improve the quality of care where possible. The ability to provide the national SHMI mortality metric based on HES-ONS linked data, with the ability for users to more closely investigate individual patient cohorts, will enhance users ability to understand and identify potential improvements in care to reduce mortality. Consultant code will also provide a further level of detail in the Stethoscope product to provide Trusts only to explore and understand the variation in care between their own consultants across Method Analytics indicator set. Access to the more detailed tool is provided securely to named subscribers only, with individual surgeons able to compare themselves to a national cohort of surgeons. Access controls restrict access to consultant-identifiable data so that only authorised staff at an individual Trust can only see data for their own employees, and such data is suppressed in line with the HES analysis guide. No access to servers containing HES data is possible through Stethoscope as the Stethoscope servers are not linked in any way to the secure environment. Only aggregated data (with small numbers suppressed in line with the HES Analysis Guide) is surfaced through Stethoscope. Many indicators are available dealing with Quality and Safety issues NHS Organisations face to allow decision makers to take actions based on up to date information. Methods have CCG, provider and NHS England regions as subscribers with over 100,000 page views per year and 100% contract renewal from subscribers indicating the value of the system to NHS users. The Stethoscope subscriber system has been used to support Quality Surveillance Groups, Quality Summits, and board to board oversight meetings This application/agreement provides Stethoscope access to non-NHS organisations solely where they are working for NHS benefit by providing service improvement support to NHS organisations. As some NHS organisations require additional specialist resource to deliver the benefits of using benchmarking information, therefore subscription to Stethoscope is required by the non-NHS organisations as: 1. This enables the non-NHS organisation to have people equipped to provide immediate support to NHS organisations. 2. Providing them with aggregate level information via the tool is the most efficient way of disseminating information in support of this work – the alternative described directly below would clearly create large inefficiencies. 3. It allows such organisations to be autonomous in undertaking work that requires a level of independence and is beneficial to the NHS and negates the risk associated with further raw data dissemination these organisations directly. Allowing select non-NHS healthcare focused organisations to access aggregate level analytics is beneficial to the NHS as it enable the NHS to quickly access additional specialist resource when it is required. This allows the timely delivery of improvements in clinical quality and/or operational efficiency. Without this option it would be necessary for them to increase or upskill their internal resource. To do so would require longer timescales and prove more costly for the organization and therefore the NHS in the long run if there is primarily a short term need. This application/agreement will extend Methods’ ability to inform providers and commissioners of NHS care and other interested parties (as described) by broadening the scope and coverage of data sets that can be used to understand the health care system and so improve their ability to help users of their services understand behaviour, activity and outcomes in the health care system, so supporting service, efficiency and quality improvement efforts. The expected benefits from DIDs data that have been considered in discussion with clients include: ability to understand patient flows and pathway duration prior to secondary care and so inform care pathway redesign, potentially reducing delay, shortening journeys, reducing the need for multiple outpatient appointments and secondary care visits, so reducing cost to the NHS and also negating the need for patients to make multiple journeys. Mental health and learning disability is not well served by healthcare intelligence intermediaries and the ability to respond to direct requests from providers, commissioners and other interested parties such as local authorities for information and analysis will be welcomed. The expected benefits from MHLDDS data that have been considered in discussion with clients (current CCG clients and prospective clients) include: broadening the use of the platform from pure secondary care to a more population health focus and understanding the complex relationship between physical and mental health through linked health sector data, which will enable the design, monitoring and management of care pathways for individuals using this multi-sector set of services. This will improve patient experience, improving population management and so efficiency, capacity and cost. 2) Methods Analytics work with Trusts and CCGs, and wider programmes such as the DH.NHS Improvement GIRFT team to provide ad hoc reporting matching their requirements, using HES/SUS and SUS PbR data as appropriate to derive insight into a specific topic or issue. The ability to extend the reporting, particularly for the GIRFT programme by using DIDs, MHLDDS and HES-ONS lined data is desirable as it means that more of the patient pathway and scope of care delivery can be considered in the program and greateer quality improvement and efficiency gains sought. A real life example is a review of urgent care within an NHS Trust: Methods Analytics used HES data to build a picture of issues around urgent care including where patients are flowing from, how referral patterns are changing over time and conversion rates that was used by the organization to initiate a transformation programme and improve urgent care timeliness and outcomes. Similar projects focusing on mortality have resulted in large and lasting reductions in hospital mortality. Projects include a large amount of clinical engagement to ensure that data in the reports is used in the best way possible to make changes to services that benefit patients in any organisation working with Methods Analytics. The Analytics team includes clinicians and consultants to provide the right expertise when discussing any insight with Methods Analytics NHS clients. Methods are also working with the DH GIRFT programme, generating report across 12 specialty areas under Lord Carters NHS efficiency programme. These detailed data rich reports are shaped by national lead clinicians for each specialty and they then visit every provider in England to discuss their data with them in order to improve the quality and efficiency of care. This programme is currently being rolled out. Methods Analytics have recently produced a programme update for the GIRFT team, using HES analysis to demonstrate the early impact the GIRFT programme has had across the NHS and supporting policy development, such as, realising over £4m of cashable saving and releasing over 50,000 bed days of surgical occupancy while improving the quality of care. The ability to bring DIDs data to bear on the GIRFT programme will enhance Methods’ ability to support them by bringing an additional area of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. Enabling Methods Analytics to place tabulations as described for free in the public domain will deliver benefit to the NHS and wider public as aggregate, anonymous, low volume suppressed data that we have created as part of the input to published academic work (e.g. http://www.iaas-med.com/files/Journal/21.4/Swift_et_al.pdf) and public reports (e.g. the NHS England surgical deep dive reports referred to in 3 below) will enable further local analysis, research and understanding of improvement science in healthcare, ultimately benefitting healthcare and the public purse. 3) The NSCC dashboards and PET tool was developed in partnership with the Royal College of Surgeons and NHS England Rightcare programme to support the work of the National Surgical Commissioning Centre http://www.rcseng.ac.uk/healthcare-bodies/nscc. They developed commissioning guides for CCGs on specific interventions which all have NICE accreditation. As part of this work, Methods Analytics developed the PET tool to allow commissioners to access data to support the guidelines. The commissioning Guides are approved by the National Institution for Clinical Excellence and together with the data tools are used by commissioners across England to improve services for patients and monitor those improvements. RCSE has made the guidelines publically available and also the data tools in line with the requirements of the governments transparency agenda. Therefore there are no ‘customers’ as the tool is available to all. This tool has been live since 2012 and Methods Analytics has been recontracted again for 2016/17, to maintain and enhance these tools. The tool receives of the order of 350 hits per month from NHS and wider public. Methods Analytics has recently produced a ‘surgical deep dive’ report for the RCSE and NHS England Rightcare programme that uses HES analysis to produce a detailed report for every NHS provider and CCG detailing variation across 29 surgical care pathways that will be available for free to the public and NHS on the NHS England web site. 4) SWORD is a tool developed with ALS and AUGIS to provide to their consultant surgeon members detailed, clinically valid metric that report activity, quality and outcome metrics for surgical pathways. The tool is now live following user validation and testing, with consultant surgeons starting to request, and being provided with, access. Wider roll out is ongoing, including developing relationships with other surgical specialties, where the Association of Coloproctologists Great Britain and Ireland and the British Association of Paediatric Surgery are working with us to develop pathways. By allowing surgeons to see how their quality of care varies from other surgeons performing the same operations they can work to improve the levels of care they are able to offer and improve the safety for patients they are operating on, in order to get a full understanding it is important they are able to identify themselves in the tool. Surgeons can also use the data could also be used for revalidation purposes therefore ensuring patient safety by providing evidence a surgeon is up to date and fit for practice. There is significant interest from other specialist societies based on the work done in upper GI and laparoscopic surgery and developmental work is underway with the Association of colorectal surgeons, British Association of Pediatric Surgeons and support from the over-arching Federation of Surgical Specialist Associations. The ability to bring DIDs data to bear on the SWORD programme will enhance Methods’ ability to support them by bringing additional areas of NHS activity and so cost into scope, enabling greater scrutiny and discussion of potential quality and efficiency opportunities. The ability to enhance SWORD with HES-ONS linked data is desired by the clinical leadership of the programme as there is a clear understanding that mortality post discharge is not currently readily available at the granular level required for consideration of individual surgeon or individual procedure performance, and bringing this additional data item into the programme will enhance the ability to identify potential reductions in surgical mortality.
MONITOR	MONITOR	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff.	Data processing activities: **PLICS: Amendment for consideration ** PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. End of Update ** • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff.	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions.	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs.
MONITOR	MONITOR	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff.	Data processing activities: **PLICS: Amendment for consideration ** PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. End of Update ** • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff.	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions.	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs.
MONITOR	MONITOR	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff.	Data processing activities: **PLICS: Amendment for consideration ** PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. End of Update ** • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff.	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions.	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs.
MONITOR	MONITOR	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff.	Data processing activities: **PLICS: Amendment for consideration ** PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. End of Update ** • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff.	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions.	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs.
MONITOR	MONITOR	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Monitor; This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: Patient-level costing NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. NHS Improvement (NHSI) have been given a mandate as per the Carter Review to seek efficiencies in the provision of imaging services within NHS Trusts. Access to the Diagnostic Imaging Dataset (DID), which is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients will support; ● The aim is to develop a set of metrics that will help NHS Improvement understand the current quality of the service ● Consultant Radiologist efficiency metrics ● Imaging services’ operational performance and workforce ● Imaging services’ financial performance ● Imaging services’ performance and financial improvement targets (including monitoring of progress against those targets) configuration of Imaging services across England, current performance, variations against best performance and a tracking mechanism for improvement. Questions involving equipment inventory have not been included as this is part of a separate workstream, however this data will be used to inform demand metrics. It is expected that the metrics would help support the development of the service and optimisation of service models. The metrics will inform an understanding of the following areas: ● Patient outcomes ● Clinical NHS Trust Development Authority (NHS TDA) NHS TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: • Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: o Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, MHLDDS and SUS PbR.	Data processing activities: PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff. For the purposes of this analysis NHSI require the full DIDs 2015/16 financial year dataset and the 2016/17 dataset when available. Data processing activities include: • Populating an imaging dashboard database, which will inform the metrics listed above. • Populating the Imaging portal on Model Hospital • NHSI staff and contractors shall create aggregated summaries and reports • Analyse the data, and any derivative will be accessed as data reports, aggregated summaries or within analysis tools. • Data will be anonymised and considered at Trust level. The NHSI Medical Directorate, the patient safety division require the data to carry out the following processing activities: • Assessing the effectiveness of services – deaths post discharge/attendance, especially at times of pressure, deaths for those in contact with Mental Health services • Monitoring the safety of services – deaths from Sepsis (via cause of death), deaths post discharge from VTE • Evaluating the patient experience – deaths in own home or hospice post discharge or attendance • Mortality oversight – cause of death for those discharged from hospital to understand variation in SHMI, variation in cause of death compared to primary admissions diagnosis NHS Trust Development Agency will only access all of the datasets within this agreement with the exception of the PLICS data for the purposes as set out in this this agreement. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. Data processing activities include: • Populating ‘dashboard’ databases. Plans and reports will be populated with metric values from the ‘dashboard’ database. • Creating aggregate summaries and reports • Analysis and any derivative produced will be accessed as data reports, aggregated summaries and/or within analysis tools • Publishing the following: - results of analysis of the data; - and data in aggregate and summary form • TDA may share data with those holding honorary contracts or sub-contract work to sub-contractors working for and on behalf of TDA. Their working arrangements will be the same as employed staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to NHSI’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the NHSI Head of Information Governance. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of NHSI’s Incident and Reporting procedure and are required to follow these. • TDA and their data processors will not disseminate NHS Digital data in the format it is received or any subset of the said data, to any third party not included in this Agreement. • Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development sharing of this information will be inline with the HES analysis guide where all small numbers will be suppressed. • Only Anonymous data or aggregated data with small number suppression will be published. • Access to the data will be restricted to people employed by or contracted to NHS TDA • Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. These results will be aggregated with small number suppressed. • Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. • Data will not be used for any commercial purpose. • The Mental Health data will also be used to develop NHSI’s mental health modules of analysis within its Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. All processing of PROMS data will be done in accordance with the PROMS terms and conditions, PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. All organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. TDA shall use pseudonymised data that will be queried, aggregated and combined in many different ways to support its objectives for processing. Some example outputs that will form part of those core functions are: • Using data to measure the performance of Trusts and assist in service re-design where necessary. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in Trusts, and, also at an executive level. • Developing the Single Oversight Framework (SOF) to provide an overall view using a range of indicators. The results are provided for regional teams, who have day-to-day responsibility and oversight of trusts. The data helps them to form an assessment of how a trust is performing. • Calculating metrics for dashboards • Calculating metrics for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications • Supporting Improvement initiatives across TDA Taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data include: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. All outputs will be subject to small number suppression in line with the HES analysis guide. TDA (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the Local Health Economy (LHE). It will be used as a module of analysis within NHSI’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite, of which consist of Monitor, NHS TDA, and NHS England (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally).	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs. Access to NHS Digital data would enable TDA to effectively carry out its statutory functions. Examples of this include delivering a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: - standards relating to quality of care, - their duty to exercise their functions efficiently, economically and effectively , and - the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. TDA will help develop Intelligence data packs that will facilitate and build internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care. Single Operating Framework Output – Framework is unpublished, but is available for use among Trusts about whom the data relates to. Of the main indicators, several are obtained from HES data: waiting times at A&E; outpatient DNA rates for children and adults; emergency re-admissions within 28 days; average length of stay for elective and emergency admissions; proportion of inpatients returning to usual place of residence. Data is extracted at a Trust level. Timeseries charts and peer to peer comparison are available for each indicator. The benefits of the SOF are that: 1. It helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. 2. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. In order to address this problem, there is ongoing work to support senior management in the DH and its arm’s length bodies with a range of indicators to try to understand what is happening, and assist in the development of solutions. One such indicator that is being developed is to compare the weekday discharge rates with weekend discharge rates. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Monitor; This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: Patient-level costing NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. NHS Improvement (NHSI) have been given a mandate as per the Carter Review to seek efficiencies in the provision of imaging services within NHS Trusts. Access to the Diagnostic Imaging Dataset (DID), which is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients will support; ● The aim is to develop a set of metrics that will help NHS Improvement understand the current quality of the service ● Consultant Radiologist efficiency metrics ● Imaging services’ operational performance and workforce ● Imaging services’ financial performance ● Imaging services’ performance and financial improvement targets (including monitoring of progress against those targets) configuration of Imaging services across England, current performance, variations against best performance and a tracking mechanism for improvement. Questions involving equipment inventory have not been included as this is part of a separate workstream, however this data will be used to inform demand metrics. It is expected that the metrics would help support the development of the service and optimisation of service models. The metrics will inform an understanding of the following areas: ● Patient outcomes ● Clinical NHS Trust Development Authority (NHS TDA) NHS TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: • Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: o Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, MHLDDS and SUS PbR.	Data processing activities: PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff. For the purposes of this analysis NHSI require the full DIDs 2015/16 financial year dataset and the 2016/17 dataset when available. Data processing activities include: • Populating an imaging dashboard database, which will inform the metrics listed above. • Populating the Imaging portal on Model Hospital • NHSI staff and contractors shall create aggregated summaries and reports • Analyse the data, and any derivative will be accessed as data reports, aggregated summaries or within analysis tools. • Data will be anonymised and considered at Trust level. The NHSI Medical Directorate, the patient safety division require the data to carry out the following processing activities: • Assessing the effectiveness of services – deaths post discharge/attendance, especially at times of pressure, deaths for those in contact with Mental Health services • Monitoring the safety of services – deaths from Sepsis (via cause of death), deaths post discharge from VTE • Evaluating the patient experience – deaths in own home or hospice post discharge or attendance • Mortality oversight – cause of death for those discharged from hospital to understand variation in SHMI, variation in cause of death compared to primary admissions diagnosis NHS Trust Development Agency will only access all of the datasets within this agreement with the exception of the PLICS data for the purposes as set out in this this agreement. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. Data processing activities include: • Populating ‘dashboard’ databases. Plans and reports will be populated with metric values from the ‘dashboard’ database. • Creating aggregate summaries and reports • Analysis and any derivative produced will be accessed as data reports, aggregated summaries and/or within analysis tools • Publishing the following: - results of analysis of the data; - and data in aggregate and summary form • TDA may share data with those holding honorary contracts or sub-contract work to sub-contractors working for and on behalf of TDA. Their working arrangements will be the same as employed staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to NHSI’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the NHSI Head of Information Governance. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of NHSI’s Incident and Reporting procedure and are required to follow these. • TDA and their data processors will not disseminate NHS Digital data in the format it is received or any subset of the said data, to any third party not included in this Agreement. • Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development sharing of this information will be inline with the HES analysis guide where all small numbers will be suppressed. • Only Anonymous data or aggregated data with small number suppression will be published. • Access to the data will be restricted to people employed by or contracted to NHS TDA • Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. These results will be aggregated with small number suppressed. • Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. • Data will not be used for any commercial purpose. • The Mental Health data will also be used to develop NHSI’s mental health modules of analysis within its Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. All processing of PROMS data will be done in accordance with the PROMS terms and conditions, PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. All organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. TDA shall use pseudonymised data that will be queried, aggregated and combined in many different ways to support its objectives for processing. Some example outputs that will form part of those core functions are: • Using data to measure the performance of Trusts and assist in service re-design where necessary. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in Trusts, and, also at an executive level. • Developing the Single Oversight Framework (SOF) to provide an overall view using a range of indicators. The results are provided for regional teams, who have day-to-day responsibility and oversight of trusts. The data helps them to form an assessment of how a trust is performing. • Calculating metrics for dashboards • Calculating metrics for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications • Supporting Improvement initiatives across TDA Taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data include: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. All outputs will be subject to small number suppression in line with the HES analysis guide. TDA (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the Local Health Economy (LHE). It will be used as a module of analysis within NHSI’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite, of which consist of Monitor, NHS TDA, and NHS England (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally).	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs. Access to NHS Digital data would enable TDA to effectively carry out its statutory functions. Examples of this include delivering a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: - standards relating to quality of care, - their duty to exercise their functions efficiently, economically and effectively , and - the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. TDA will help develop Intelligence data packs that will facilitate and build internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care. Single Operating Framework Output – Framework is unpublished, but is available for use among Trusts about whom the data relates to. Of the main indicators, several are obtained from HES data: waiting times at A&E; outpatient DNA rates for children and adults; emergency re-admissions within 28 days; average length of stay for elective and emergency admissions; proportion of inpatients returning to usual place of residence. Data is extracted at a Trust level. Timeseries charts and peer to peer comparison are available for each indicator. The benefits of the SOF are that: 1. It helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. 2. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. In order to address this problem, there is ongoing work to support senior management in the DH and its arm’s length bodies with a range of indicators to try to understand what is happening, and assist in the development of solutions. One such indicator that is being developed is to compare the weekday discharge rates with weekend discharge rates. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Monitor; This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: Patient-level costing NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. NHS Improvement (NHSI) have been given a mandate as per the Carter Review to seek efficiencies in the provision of imaging services within NHS Trusts. Access to the Diagnostic Imaging Dataset (DID), which is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients will support; ● The aim is to develop a set of metrics that will help NHS Improvement understand the current quality of the service ● Consultant Radiologist efficiency metrics ● Imaging services’ operational performance and workforce ● Imaging services’ financial performance ● Imaging services’ performance and financial improvement targets (including monitoring of progress against those targets) configuration of Imaging services across England, current performance, variations against best performance and a tracking mechanism for improvement. Questions involving equipment inventory have not been included as this is part of a separate workstream, however this data will be used to inform demand metrics. It is expected that the metrics would help support the development of the service and optimisation of service models. The metrics will inform an understanding of the following areas: ● Patient outcomes ● Clinical NHS Trust Development Authority (NHS TDA) NHS TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: • Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: o Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, MHLDDS and SUS PbR.	Data processing activities: PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff. For the purposes of this analysis NHSI require the full DIDs 2015/16 financial year dataset and the 2016/17 dataset when available. Data processing activities include: • Populating an imaging dashboard database, which will inform the metrics listed above. • Populating the Imaging portal on Model Hospital • NHSI staff and contractors shall create aggregated summaries and reports • Analyse the data, and any derivative will be accessed as data reports, aggregated summaries or within analysis tools. • Data will be anonymised and considered at Trust level. The NHSI Medical Directorate, the patient safety division require the data to carry out the following processing activities: • Assessing the effectiveness of services – deaths post discharge/attendance, especially at times of pressure, deaths for those in contact with Mental Health services • Monitoring the safety of services – deaths from Sepsis (via cause of death), deaths post discharge from VTE • Evaluating the patient experience – deaths in own home or hospice post discharge or attendance • Mortality oversight – cause of death for those discharged from hospital to understand variation in SHMI, variation in cause of death compared to primary admissions diagnosis NHS Trust Development Agency will only access all of the datasets within this agreement with the exception of the PLICS data for the purposes as set out in this this agreement. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. Data processing activities include: • Populating ‘dashboard’ databases. Plans and reports will be populated with metric values from the ‘dashboard’ database. • Creating aggregate summaries and reports • Analysis and any derivative produced will be accessed as data reports, aggregated summaries and/or within analysis tools • Publishing the following: - results of analysis of the data; - and data in aggregate and summary form • TDA may share data with those holding honorary contracts or sub-contract work to sub-contractors working for and on behalf of TDA. Their working arrangements will be the same as employed staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to NHSI’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the NHSI Head of Information Governance. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of NHSI’s Incident and Reporting procedure and are required to follow these. • TDA and their data processors will not disseminate NHS Digital data in the format it is received or any subset of the said data, to any third party not included in this Agreement. • Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development sharing of this information will be inline with the HES analysis guide where all small numbers will be suppressed. • Only Anonymous data or aggregated data with small number suppression will be published. • Access to the data will be restricted to people employed by or contracted to NHS TDA • Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. These results will be aggregated with small number suppressed. • Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. • Data will not be used for any commercial purpose. • The Mental Health data will also be used to develop NHSI’s mental health modules of analysis within its Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. All processing of PROMS data will be done in accordance with the PROMS terms and conditions, PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. All organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. TDA shall use pseudonymised data that will be queried, aggregated and combined in many different ways to support its objectives for processing. Some example outputs that will form part of those core functions are: • Using data to measure the performance of Trusts and assist in service re-design where necessary. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in Trusts, and, also at an executive level. • Developing the Single Oversight Framework (SOF) to provide an overall view using a range of indicators. The results are provided for regional teams, who have day-to-day responsibility and oversight of trusts. The data helps them to form an assessment of how a trust is performing. • Calculating metrics for dashboards • Calculating metrics for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications • Supporting Improvement initiatives across TDA Taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data include: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. All outputs will be subject to small number suppression in line with the HES analysis guide. TDA (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the Local Health Economy (LHE). It will be used as a module of analysis within NHSI’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite, of which consist of Monitor, NHS TDA, and NHS England (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally).	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs. Access to NHS Digital data would enable TDA to effectively carry out its statutory functions. Examples of this include delivering a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: - standards relating to quality of care, - their duty to exercise their functions efficiently, economically and effectively , and - the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. TDA will help develop Intelligence data packs that will facilitate and build internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care. Single Operating Framework Output – Framework is unpublished, but is available for use among Trusts about whom the data relates to. Of the main indicators, several are obtained from HES data: waiting times at A&E; outpatient DNA rates for children and adults; emergency re-admissions within 28 days; average length of stay for elective and emergency admissions; proportion of inpatients returning to usual place of residence. Data is extracted at a Trust level. Timeseries charts and peer to peer comparison are available for each indicator. The benefits of the SOF are that: 1. It helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. 2. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. In order to address this problem, there is ongoing work to support senior management in the DH and its arm’s length bodies with a range of indicators to try to understand what is happening, and assist in the development of solutions. One such indicator that is being developed is to compare the weekday discharge rates with weekend discharge rates. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Monitor; This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. The PROMs data is only ever available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS, and this work falls into that category. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: Patient-level costing NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. NHS Improvement (NHSI) have been given a mandate as per the Carter Review to seek efficiencies in the provision of imaging services within NHS Trusts. Access to the Diagnostic Imaging Dataset (DID), which is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients will support; ● The aim is to develop a set of metrics that will help NHS Improvement understand the current quality of the service ● Consultant Radiologist efficiency metrics ● Imaging services’ operational performance and workforce ● Imaging services’ financial performance ● Imaging services’ performance and financial improvement targets (including monitoring of progress against those targets) configuration of Imaging services across England, current performance, variations against best performance and a tracking mechanism for improvement. Questions involving equipment inventory have not been included as this is part of a separate workstream, however this data will be used to inform demand metrics. It is expected that the metrics would help support the development of the service and optimisation of service models. The metrics will inform an understanding of the following areas: ● Patient outcomes ● Clinical NHS Trust Development Authority (NHS TDA) NHS TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: • Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: o Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, MHLDDS and SUS PbR.	Data processing activities: PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data was collected during July/August 2016 – September 2016 and was used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot looked to establish clear mechanisms for safely transferring data to Monitor. The pilot has now completed and a further request has been made to NHS Digital Under section 255 of the HSCA, to request that NHS Digital continues to establish and operate a system for the collection and analysis of PLICS data. This system will build on the Pilot System Request undertaken by NHS Digital from June 2016 that concluded in October 2016. To build on the system established by the Pilot System Request, NHS Improvement would like to increase the number of trusts from which data are to be collected and analysed. • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - In addition to contracting with Data Processors, Monitor may also sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or RNOH. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. Access to the data is restricted to those parties named in this agreement namely NHS Trust Development Authority and Monitor working together under a new partnership called NHS Improvement and NHS England for the purposes of developing the national tariff, and RNOH to work on the Getting It Right First Time (GIRFT). Data sharing from Monitor to NHS TDA and NHS England for the purpose of working on the national tariff only. Monitor keep a list of approved users and audit the list every 3 months as well as updating the list every month for additional approved users and leavers. PROMS TERMS AND CONDITIONS Please note that PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff. For the purposes of this analysis NHSI require the full DIDs 2015/16 financial year dataset and the 2016/17 dataset when available. Data processing activities include: • Populating an imaging dashboard database, which will inform the metrics listed above. • Populating the Imaging portal on Model Hospital • NHSI staff and contractors shall create aggregated summaries and reports • Analyse the data, and any derivative will be accessed as data reports, aggregated summaries or within analysis tools. • Data will be anonymised and considered at Trust level. The NHSI Medical Directorate, the patient safety division require the data to carry out the following processing activities: • Assessing the effectiveness of services – deaths post discharge/attendance, especially at times of pressure, deaths for those in contact with Mental Health services • Monitoring the safety of services – deaths from Sepsis (via cause of death), deaths post discharge from VTE • Evaluating the patient experience – deaths in own home or hospice post discharge or attendance • Mortality oversight – cause of death for those discharged from hospital to understand variation in SHMI, variation in cause of death compared to primary admissions diagnosis NHS Trust Development Agency will only access all of the datasets within this agreement with the exception of the PLICS data for the purposes as set out in this this agreement. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. Data processing activities include: • Populating ‘dashboard’ databases. Plans and reports will be populated with metric values from the ‘dashboard’ database. • Creating aggregate summaries and reports • Analysis and any derivative produced will be accessed as data reports, aggregated summaries and/or within analysis tools • Publishing the following: - results of analysis of the data; - and data in aggregate and summary form • TDA may share data with those holding honorary contracts or sub-contract work to sub-contractors working for and on behalf of TDA. Their working arrangements will be the same as employed staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to NHSI’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the NHSI Head of Information Governance. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of NHSI’s Incident and Reporting procedure and are required to follow these. • TDA and their data processors will not disseminate NHS Digital data in the format it is received or any subset of the said data, to any third party not included in this Agreement. • Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development sharing of this information will be inline with the HES analysis guide where all small numbers will be suppressed. • Only Anonymous data or aggregated data with small number suppression will be published. • Access to the data will be restricted to people employed by or contracted to NHS TDA • Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. These results will be aggregated with small number suppressed. • Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. • Data will not be used for any commercial purpose. • The Mental Health data will also be used to develop NHSI’s mental health modules of analysis within its Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. All processing of PROMS data will be done in accordance with the PROMS terms and conditions, PROMs data is only available for non-commercial purposes, such as academic research, or in connection with delivering services to the NHS. All organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). PLICS: NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard; NHS Improvement are in the process of creating the Weighted Activity Unit using PLICS data and identifying ‘potential savings opportunities’ (in line with the Carter methodology) which will allow drill down into cost data to see what the issue or opportunity is. The pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. TDA shall use pseudonymised data that will be queried, aggregated and combined in many different ways to support its objectives for processing. Some example outputs that will form part of those core functions are: • Using data to measure the performance of Trusts and assist in service re-design where necessary. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in Trusts, and, also at an executive level. • Developing the Single Oversight Framework (SOF) to provide an overall view using a range of indicators. The results are provided for regional teams, who have day-to-day responsibility and oversight of trusts. The data helps them to form an assessment of how a trust is performing. • Calculating metrics for dashboards • Calculating metrics for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications • Supporting Improvement initiatives across TDA Taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data include: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. All outputs will be subject to small number suppression in line with the HES analysis guide. TDA (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the Local Health Economy (LHE). It will be used as a module of analysis within NHSI’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite, of which consist of Monitor, NHS TDA, and NHS England (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally).	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. PLICS The information gathered from this programme will be used to enable NHS Improvement through Monitor to perform its pricing and licensing functions under the HSCA more effectively. It will:  inform new methods of pricing NHS services;  inform new approaches and other changes to the design of the currencies used to price NHS services;  inform the relationship between provider characteristics and cost;  help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence;  identify the relationship between patient characteristics and cost; and  support an approach to benchmarking for regulatory purposes. The 2016 Pilot Collection of Patient Level Cost data at six acute Trusts proved that the draft patient level costing standards can be successfully implemented by NHS providers and that the process for data collection by NHS Digital for onward transmission to NHS Improvement can be completed successfully. This pilot therefore provided a proof of concept for the methodology and process. A prototype portal to enable the pilot Trusts to use the data collected to benchmark costs is under development in partnership with those Trusts and will be ready by the end of March 2017 at which point the Trusts are ready to start to engage clinicians with the data. The alignment of PLICS outputs with the Operational Productivity programme is key to benefits realisation. The data collected has already allowed NHS Improvement to link individual patient episode costs across different care settings. This is a key enabler for the development of new models of care and sustainable delivery of services. While it is too early to identify specific benefits arising from benchmarking across Trusts linked to the PLICS data collected in 2016 (and there will be limitations in the quality of the data collected in that pilot), case study evidence continues to confirm the value of patient level costs within each Trust for identifying efficiencies and service improvements, such that NHS Improvement continue to be confident that rolling out a consistent patient level methodology across all providers can derive significant benefits. NHS Improvement know of pilot sites which use the PLICS data created in 2016 to improve decision making for A&E; NHS Improvement have also received feedback that PLICS data provides more rapid outputs for operational decisions at a Trust level. This general picture was confirmed by the recent “mid-point review” of the Costing Transformation Programme, including senior stakeholders across Arm’s Length Bodies, including representatives of the Operational Efficiency Programme, GIRFT, along with representatives of providers and clinicians, continues to support the move to PLICS. It is also worth noting that a subset of Trusts will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. To develop further the works of Model Hospital and GIRFT, NHS Improvement seeks to collect and use the HES ONS bridge file to measure mortality following procedures. One of the key clinical quality metrics is death following surgical procedures. The programmes currently use HES data to calculate a number of clinical quality indicators, including in-hospital mortality. Mortality rate can be used as an indicator of poor surgical delivery. NHS Improvement anticipate that high mortality rates will correlate with high infection rates, lengths of stay and readmission rates. By focussing on the quality of surgical delivery, NHS Improvement would expect to support hospital Trusts to improve surgical quality and reduce costs. Access to NHS Digital data would enable TDA to effectively carry out its statutory functions. Examples of this include delivering a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: - standards relating to quality of care, - their duty to exercise their functions efficiently, economically and effectively , and - the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. TDA will help develop Intelligence data packs that will facilitate and build internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care. Single Operating Framework Output – Framework is unpublished, but is available for use among Trusts about whom the data relates to. Of the main indicators, several are obtained from HES data: waiting times at A&E; outpatient DNA rates for children and adults; emergency re-admissions within 28 days; average length of stay for elective and emergency admissions; proportion of inpatients returning to usual place of residence. Data is extracted at a Trust level. Timeseries charts and peer to peer comparison are available for each indicator. The benefits of the SOF are that: 1. It helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. 2. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. In order to address this problem, there is ongoing work to support senior management in the DH and its arm’s length bodies with a range of indicators to try to understand what is happening, and assist in the development of solutions. One such indicator that is being developed is to compare the weekday discharge rates with weekend discharge rates. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Episode and Spell level grouper results; underlying patient level data.	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	To inform the decision making process for determination of the scope and structure of the future Grouper Products
MONITOR	MONITOR	Patient Level Costing data (PLICS)	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This is for the purpose of fulfilling Monitor’s statutory duties. To do this, Monitor requires access to HES, SUS PbR, PROMS, and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of Monitor’s role prescribed under the Health and Social Care Act 2012 (the “2012 Act”). Specifically: - Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. This includes; ****Update Jan 17 • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. ******* • Studying how a failing provider's activity could be re-directed to other hospitals. - Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Investigating the effects of potential tariff changes on Local Health Economies • Developing new reimbursement currencies • Analysing and validating the national priced payment by results (“PbR”) activity for any given provider - Promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) - Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers - Developing modules of analyses and understanding relationships between health care provision and acute secondary services across any given Local Health Economies (Part3, Chapter 1 of the 2012 Act). Monitor change their working pattern frequently as part of investigating future models/projects. Previously Monitor used the HES and SUS PbR data to calculate the pricing analysis and improvements made to the model and analysis therefore means that PROMS is now required for pricing. PROMS will also be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to have a wider range of data in order to assess the performance of trusts. Having the linked PROMS would enable impact analysis of new outcome based payment models for in hospital services and therefore would assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. Having Mental Health data of a sensitive nature will enable Monitor to understand the relationship between mental health care and acute secondary services across all Local Health Economies (LHE) in England. Casemix HES: Under the Health and Social Care Act 2012, Monitor has a statutory duty to publish the national tariff, which is the system for NHS services. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: (a) health care services which are or may be provided for the purposes of the NHS, (b) the method used for determining national prices (c) the national price of each of those services (d) the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices). (e) the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price PLICS: NHS Improvement/Monitor’s Costing Transformation Programme (CTP), was established to implement PLICS across Acute, Mental Health, Ambulance and Community providers and. The programme entails: • Introducing and implementing new standards for patient level costing; • Developing and implementing one single national cost collection to replace current multiple collections; • Establishing the minimum required standards for costing software and promoting its adoption; and • Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff.	Data processing activities include: **Amendment Jan 2017** • Populating the Model Hospital dashboard and GIRFT programme ‘dashboard’ databases. Plans and reports identified above will be populated with metric values from the ‘dashboard’ database. Data may be extracted from the ‘dashboard’ database and provided to a third-party organisation who will then produce publications. In this case the following rules will apply: o The third-party organisation must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle HES/SUS data. o The third-party organisation will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The third-party will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital. ****** Monitor staff will: - Create aggregated summaries and reports of the data - Analyse the data, and any derivatives works Monitor produce, for the purposes outlined in the previous section. Data will be accessed as data reports, aggregated summaries or within analysis tools - Link the NHS Digital data with Casemix HES data and analyse them as part of Monitor’s role to develop the national tariff • Linking will be done at patient level but this will only consist of pseudonymised data. - Share the aggregated (data may be suppressed, take the form of indicators or gone through a cleansing process) data Monitor produce, and/or the results of Monitor’s analysis of the raw data with NHS England as part of Monitor’s joint role to develop the national tariff (NHSE have a separate license agreement with HSCIC for the raw data) - Publish the following: • results of Monitor’s analysis of the data; • and data in aggregated and summary form - Monitor may sub-contract work to sub-contractors working for and on behalf of Monitor. Their working arrangements will be the same as employed Monitor staff. They will sign up to the same Terms and Conditions as all permanent/temporary staff (as well as those confidentiality and data protection policies of their Agents). All data will be accessed via the same systems which Monitor staff access the data. Training and IG induction sessions are mandatory before anyone can access the data. Access to any IT and any data held therein is provided according to Monitor’s Access Control policy. Any NHS Digital data are only ever accessed by those who are fulfilling a purpose stated in the DSA and this is approved by the Information Governance Manager. When there is no longer a requirement for any sub-contractor to have access to the data, permissions are immediately revoked. Where there are any incidents or near misses the subcontractors are made aware of Monitors’ Incident and Reporting procedure. - Data is processed to produce the required outputs and the development of SSIS packages to group data. Further processing is conducted in analytic and statistical applications - Monitor (and their Data Processors) will not disseminate data In the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. - Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymous data - Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development In addition, the Mental Health data will also be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Casemix HES: The data will be used for research and analysis into pricing, including the impacts of pricing alternatives on stakeholders in the health sector. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES MMES, SUS PbR, PROMS, and/or MHLLDS data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at a aggregated level and with small numbers suppressed in line with the HES analysis guide. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarized data as well as the results of the analysis will ultimately be made public. Monitor will only publish analytical anonymised data. Access to the data will be restricted to people employed by or contracted to Monitor, NHS TDA, or NHS England. Results of the analysis may be shared with colleagues at NHS Digital/DH/NHSE to inform future policy development. Monitor will not use data for any commercial purpose. PLICS: PLICS data will be linked with HES data as provided in this agreement. This will be via the Episode number key. To facilitate the development of a successful PLICS data collection system in the first instance, the following volunteer providers have agreed to participate in a pilot collection between July/August 2016 and September 2016. • Buckinghamshire Healthcare NHS Trust • Guy’s and St Thomas’ NHS Foundation Trust • The Royal Free London NHS Foundation Trust • The Royal Marsden NHS Foundation Trust • The Royal Orthopaedic Hospital NHS Foundation Trust • University Hospitals Birmingham NHS Foundation Trust • Chelsea and Westminster NHS Foundation Trust PLICs data shall be collected during July/August 2016 – September 2016 and will be used to test the ability of the system to successfully collect, collate, link, pseudonymise and validate data. Furthermore the pilot will look to establish clear mechanisms for safely transferring data to Monitor. The processing activities here are limited solely to the pilot relating to the seven named Trusts.	Monitor is requesting permission to receive data without identifiers that will be queried, aggregated and combined in many different ways to support its objectives for processing above. Some example outputs that will form part of those core functions are: ** Amendment update Jan 2017** Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, we expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. ******************** - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, are: December 2014 https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients This report summarises the findings of NHS England, Monitor and the NHS Trust Development Authority’s joint project to support 11 local health economies to develop clinically and financially sustainable 5-year strategic plans. June 2015 https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver This document looks at the progress made towards the ‘Five Year Forward View’, and sets out the next steps needed to be taken to achieve these shared ambitions. The paper starts a period of engagement with the NHS, patients and other partners on how to respond to the long-term challenges and close the health and wellbeing gap; the care and quality gap; and the funding and efficiency gap. February 2016 https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. All outputs will be subject to small number suppression in line with the HES analysis guide. Monitor (and their Data Processors) will not supply record level data to any third party, and the data will not form part of any tool, product or analytical output which is made available on a commercial basis. The Mental Health dataset will also generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. It will be used as a module of analysis within Monitor’s LHE Intelligence Unit data packs that are used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). The analytical outcomes/outputs of analysis will be shared with regional monitoring teams, and possible regional Tripartite (if outputs identify an issue that should be addressed in the LHE). (No underlying data will be shared with third parties or externally). Casemix HES: Within the period of the agreement only, Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. Monitor will hold the Intellectual Property Rights in the production of the National tariff and any derivative works from it.	Having access to NHS Digital data would enable Monitor to effectively fulfil its regulatory responsibilities and statutory obligations. Examples of this include delivering a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners. Monitor are also working on the development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff. PROMS data will benefit healthcare by enabling a better more effective payment system which in turn would not just the users but all of the NHS. Mental Health data will enable development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. The outputs in Monitor’s Local Health Economy Intelligence data packs, will facilitate and build Monitor’s internal knowledge of the relationship between Mental Health and Physical Health care across each LHE in England. This will support regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level. Casemix HES: The National Tariff allows providers of NHS care to be reimbursed for care provision under the PBR Policy. The information gathered from the PLICS is programme will be used to enable NHS Improvement to perform its pricing and licensing functions under the HSCA more effectively. It will: • inform new methods of pricing NHS services; • inform new approaches and other changes to the design of the currencies used to price NHS services; • inform the relationship between provider characteristics and cost; • help trusts to maximise use of their resources and improve efficiencies, as required by the provider licence; • identify the relationship between patient characteristics and cost; and support an approach to benchmarking for regulatory purposes. **Amendment Jan 2017** The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020. ******
MONITOR	MONITOR	Standard Monthly Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. This application seeks to request data for both the TDA and Monitor as Data controllers in common. The TDA is a Special Health Authority established by Article 2 of the TDA (Establishment and Constitution) Order 2012. The NHS TDA is also made up of the Patient Safety, the National Reporting and Learning System, the Advancing Change and the Intensive Support Teams. Under the NHS DA (Directions and Miscellaneous Amendments etc) Regulations 2016 it has a general power to take such steps as it considers necessary and appropriate to assist and support persons providing NHS services to ensure continuous improvement in the quality of the provision and the financial sustainability of NHS services. Monitor is a statutory body. Under the Health and Social Care Act 2012. It has a duty when exercising its functions to protect and promote patient interests by promoting economic, efficient and effective health care services whilst maintaining or improving quality. Monitor must co-operate with Special Health Authorities including the NHS TDA. Monitor and the NHS Trust Development Authority (TDA) have come together under the operational name NHS Improvement, combining the functions and responsibilities of the 2 statutory bodies in a single integrated organisation. As such, NHS Improvement is responsible, among other things, for the oversight of NHS trusts, NHS foundation trusts and independent providers. Monitor require access to the following data sets as part of this request; Hospital Episode Statistics (HES) Mental Health Data Sets (MHMDS) (MHLDDS) (MHSDS) Secondary Uses Service Payment By results (SUS Pbr) Patient Reported Outcome Measures (PROMS) Diagnostic Imaging Data Set (DiDs) Patient Level Costing Data (PLICS) data will also be shared through this agreement for both Acute and Mental Health. The purposes for access are; (1) Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. And, promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) This includes; • The Costing Transformation Programme (CTP), was established to implement Patient Level Information Costing System (PLICS) across Acute, Mental Health, Ambulance and Community providers and. The programme entails: a. Introducing and implementing new standards for patient level costing. b. Developing and implementing one single national cost collection to replace current multiple collections; c. Establishing the minimum required standards for costing software and promoting its adoption; and d. Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve and develop products to help support service improvements within hospitals. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital and develop products to help support service improvements within hospitals. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. (2) Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Monitor has a statutory duty to publish the national tariff. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: a. health care services which are or may be provided for the purposes of the NHS b. the method used for determining national price c. the national price of each of those service d. the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices) e. the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price. (3) Monitor change their working pattern frequently as part of investigating future models/projects. Monitor uses HES and SUS PbR data to calculate the pricing analysis and improvement models. PROMS is also required for pricing analysis. PROMS will be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to assess the performance of trusts. Linked PROMS data will enable impact analysis of new outcome based payment models for in hospital services and therefore will assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. (4) Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • Assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers (5) Monitor will share the analysis and underlying data back with the trusts about whom the data pertains. Monitor will notify NHS Digital of each trust as and when a merger is being risk assessed by Monitor. Any such access/sharing of data would only take place where the provider has an existing DSA for HES data in place with NHS Digital. Before any access/sharing of analysis and data with trusts takes place, NHS Improvement will ensure that suitable controls are in place by reviewing the trusts security arrangements and entering into a DSA such that the HES data is used by the Trust solely in line with the purposes set out within the agreement. (6) Monitor requires the HES CIP as a metric calculation from NHS Digital and wish to use this as part of Monitor’s remit in developing the Single Oversight Framework (SOF) for trusts. Monitor are standardising their methodology in SOF to calculate re-admission metric as per national definition, which is to calculate readmissions from Continuous Inpatient Spells. The purpose of the SOF is to help identify where providers may benefit from, or require, improvement support, to meet the standards required of them in a safe and sustainable way. It sets out how NHSI identify providers’ potential support needs, and determines the way they work with each provider to ensure appropriate support is made available where required. There are a number of NHS Digital data sets used to develop metrics in the SOF, this is an additional metric to help measure Emergency readmissions within 30 days of discharge from hospital. NHS Trust Development Authority (NHS TDA) TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: (7) Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: • Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, Mental health data and SUS PbR. NHS Improvement and the Royal National Orthopaedic Hospital NHS Trust (RNOH) are working together to develop and expand the Getting it Right First Time Programme, which is a programme to improve the productivity, efficiency and quality of care of NHS providers. As part of that programme, RNOH wishes to analyse a wide range of data about NHS providers, including information about their operations, performance and costs in order to formulate appropriate metrics for benchmarking analysis and identify means of improvements to help shape discussions with hospital clinicians and managers, and help encourage the development of improvement plans for hospitals.	Mental Health data will be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. HES data will be used to help NHSI perform its role in helping trusts navigate the regulatory issues surrounding a transaction under the umbrella of NHSI and helping trusts to provide better care. To help trusts identify any possible competition issues with a proposed merger, NHSI undertakes analysis in line with the approach. Data on elective activity (outpatient and admitted patient) is central to this analysis. Using an NHSI developed improvement tool, it has developed an approach to process and analyse the data over the recent years and can conduct the analysis very efficiently. The development of an internal Monitor data extraction tool (‘HES Browser’) provides an opportunity to achieve the desired efficiency gains in this process. The tool (when fully operational) is expected to enable Monitor to process HES data efficiently and carry out the analysis quickly. However the data processed with HES Browser comes from NHSD and is subject to Monitor’s contract with NHSD. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES, SUS PbR, PROMS, and/or Mental Health data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at an aggregated level and with small numbers suppressed in line with the HES analysis guide. Building on the Acute PLICS pilot collections, Monitor is working with NHS Digital to establish a PLICS Mental Health system to successfully collect, collate, link, pseudonymise and validate data. The pilot shall look to establish clear mechanisms for safely transferring pseudonymised linked PLICS MHSDS data back to Monitor. A request made to NHS Digital under section 255 of the Health and Social Care Act, to establish and operate a system for the collection and analysis of PLICS mental health data was accepted on 6th September 2017. The pseudonymised MHSDS PLICS data will be held on the PLICS portal hosted on the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked MHSDS PLICS data and record level PLICS data to individual Trusts who participate in the PLICS mental health pilot. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked PLICS data and record level PLICS data to individual Trusts who participated in the PLICS pilot collections. PLICS data shall include non-sensitive, non-identifiable fields relating to that Trust. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. The use of linked PLICS data is also required for the development of GIRFT data packs, GIRFT national reports, and products to support the work of regional GIRFT and NHS I implementation teams. These plans, reports will be populated with metric values from the PLICS portal. Data may be extracted from the portal and provided back to the submitting Trust. In this case the following rules will apply: o The Trust must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle NHS Digital data. o The Trust will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The Trust will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital i.e. who were the original data owners. NHS Improvement will ensure that suitable controls are in place such that the data is used by the Trust solely in line with the purposes set out within the agreement. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor and the TDA will only publish analytical anonymous data with small numbers suppressed. Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development. Monitor and the NHS TDA (and their Data Processors) will not disseminate data in the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. Access to all data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or Royal National Orthopaedic Hospital. The Royal National Orthopaedic Hospital NHS Trust (RNOH), are included as a data processor however processing will only occur at the specified Monitor location of Wellington House. No data will flow to RNOH. Monitor will not use data for any commercial purpose. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff, PLICS, GIRFT data packs and Model Hospital Dashboard outputs. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Example outputs that will form part of the core functions set out in the purpose section are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, NHSI expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. The Mental Health dataset will generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. They will be used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). PLICS pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. Data are to be used to calculating metrics for dashboards and in particular for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications. HES CIP data will be used to develop the the Single Oversight Framework (SOF) for trusts. Data are also used to support Improvement initiatives across TDA such as taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data. Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, include: https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver	Benefits which will be achieved from having access to the data requested are; • Enabling the delivery of a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners • Development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff • Enabling a better more effective payment system which in turn would not just the users but all of the NHS • Enabling the development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. supporting regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level • The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020 Access to the data will also enable the TDA to deliver a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: • standards relating to quality of care • their duty to exercise their functions efficiently, economically and effectively • the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable • Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care • Single Operating Framework Output helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Standard Monthly Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. This application seeks to request data for both the TDA and Monitor as Data controllers in common. The TDA is a Special Health Authority established by Article 2 of the TDA (Establishment and Constitution) Order 2012. The NHS TDA is also made up of the Patient Safety, the National Reporting and Learning System, the Advancing Change and the Intensive Support Teams. Under the NHS DA (Directions and Miscellaneous Amendments etc) Regulations 2016 it has a general power to take such steps as it considers necessary and appropriate to assist and support persons providing NHS services to ensure continuous improvement in the quality of the provision and the financial sustainability of NHS services. Monitor is a statutory body. Under the Health and Social Care Act 2012. It has a duty when exercising its functions to protect and promote patient interests by promoting economic, efficient and effective health care services whilst maintaining or improving quality. Monitor must co-operate with Special Health Authorities including the NHS TDA. Monitor and the NHS Trust Development Authority (TDA) have come together under the operational name NHS Improvement, combining the functions and responsibilities of the 2 statutory bodies in a single integrated organisation. As such, NHS Improvement is responsible, among other things, for the oversight of NHS trusts, NHS foundation trusts and independent providers. Monitor require access to the following data sets as part of this request; Hospital Episode Statistics (HES) Mental Health Data Sets (MHMDS) (MHLDDS) (MHSDS) Secondary Uses Service Payment By results (SUS Pbr) Patient Reported Outcome Measures (PROMS) Diagnostic Imaging Data Set (DiDs) Patient Level Costing Data (PLICS) data will also be shared through this agreement for both Acute and Mental Health. The purposes for access are; (1) Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. And, promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) This includes; • The Costing Transformation Programme (CTP), was established to implement Patient Level Information Costing System (PLICS) across Acute, Mental Health, Ambulance and Community providers and. The programme entails: a. Introducing and implementing new standards for patient level costing. b. Developing and implementing one single national cost collection to replace current multiple collections; c. Establishing the minimum required standards for costing software and promoting its adoption; and d. Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve and develop products to help support service improvements within hospitals. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital and develop products to help support service improvements within hospitals. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. (2) Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Monitor has a statutory duty to publish the national tariff. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: a. health care services which are or may be provided for the purposes of the NHS b. the method used for determining national price c. the national price of each of those service d. the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices) e. the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price. (3) Monitor change their working pattern frequently as part of investigating future models/projects. Monitor uses HES and SUS PbR data to calculate the pricing analysis and improvement models. PROMS is also required for pricing analysis. PROMS will be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to assess the performance of trusts. Linked PROMS data will enable impact analysis of new outcome based payment models for in hospital services and therefore will assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. (4) Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • Assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers (5) Monitor will share the analysis and underlying data back with the trusts about whom the data pertains. Monitor will notify NHS Digital of each trust as and when a merger is being risk assessed by Monitor. Any such access/sharing of data would only take place where the provider has an existing DSA for HES data in place with NHS Digital. Before any access/sharing of analysis and data with trusts takes place, NHS Improvement will ensure that suitable controls are in place by reviewing the trusts security arrangements and entering into a DSA such that the HES data is used by the Trust solely in line with the purposes set out within the agreement. (6) Monitor requires the HES CIP as a metric calculation from NHS Digital and wish to use this as part of Monitor’s remit in developing the Single Oversight Framework (SOF) for trusts. Monitor are standardising their methodology in SOF to calculate re-admission metric as per national definition, which is to calculate readmissions from Continuous Inpatient Spells. The purpose of the SOF is to help identify where providers may benefit from, or require, improvement support, to meet the standards required of them in a safe and sustainable way. It sets out how NHSI identify providers’ potential support needs, and determines the way they work with each provider to ensure appropriate support is made available where required. There are a number of NHS Digital data sets used to develop metrics in the SOF, this is an additional metric to help measure Emergency readmissions within 30 days of discharge from hospital. NHS Trust Development Authority (NHS TDA) TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: (7) Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: • Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, Mental health data and SUS PbR. NHS Improvement and the Royal National Orthopaedic Hospital NHS Trust (RNOH) are working together to develop and expand the Getting it Right First Time Programme, which is a programme to improve the productivity, efficiency and quality of care of NHS providers. As part of that programme, RNOH wishes to analyse a wide range of data about NHS providers, including information about their operations, performance and costs in order to formulate appropriate metrics for benchmarking analysis and identify means of improvements to help shape discussions with hospital clinicians and managers, and help encourage the development of improvement plans for hospitals.	Mental Health data will be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. HES data will be used to help NHSI perform its role in helping trusts navigate the regulatory issues surrounding a transaction under the umbrella of NHSI and helping trusts to provide better care. To help trusts identify any possible competition issues with a proposed merger, NHSI undertakes analysis in line with the approach. Data on elective activity (outpatient and admitted patient) is central to this analysis. Using an NHSI developed improvement tool, it has developed an approach to process and analyse the data over the recent years and can conduct the analysis very efficiently. The development of an internal Monitor data extraction tool (‘HES Browser’) provides an opportunity to achieve the desired efficiency gains in this process. The tool (when fully operational) is expected to enable Monitor to process HES data efficiently and carry out the analysis quickly. However the data processed with HES Browser comes from NHSD and is subject to Monitor’s contract with NHSD. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES, SUS PbR, PROMS, and/or Mental Health data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at an aggregated level and with small numbers suppressed in line with the HES analysis guide. Building on the Acute PLICS pilot collections, Monitor is working with NHS Digital to establish a PLICS Mental Health system to successfully collect, collate, link, pseudonymise and validate data. The pilot shall look to establish clear mechanisms for safely transferring pseudonymised linked PLICS MHSDS data back to Monitor. A request made to NHS Digital under section 255 of the Health and Social Care Act, to establish and operate a system for the collection and analysis of PLICS mental health data was accepted on 6th September 2017. The pseudonymised MHSDS PLICS data will be held on the PLICS portal hosted on the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked MHSDS PLICS data and record level PLICS data to individual Trusts who participate in the PLICS mental health pilot. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked PLICS data and record level PLICS data to individual Trusts who participated in the PLICS pilot collections. PLICS data shall include non-sensitive, non-identifiable fields relating to that Trust. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. The use of linked PLICS data is also required for the development of GIRFT data packs, GIRFT national reports, and products to support the work of regional GIRFT and NHS I implementation teams. These plans, reports will be populated with metric values from the PLICS portal. Data may be extracted from the portal and provided back to the submitting Trust. In this case the following rules will apply: o The Trust must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle NHS Digital data. o The Trust will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The Trust will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital i.e. who were the original data owners. NHS Improvement will ensure that suitable controls are in place such that the data is used by the Trust solely in line with the purposes set out within the agreement. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor and the TDA will only publish analytical anonymous data with small numbers suppressed. Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development. Monitor and the NHS TDA (and their Data Processors) will not disseminate data in the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. Access to all data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or Royal National Orthopaedic Hospital. The Royal National Orthopaedic Hospital NHS Trust (RNOH), are included as a data processor however processing will only occur at the specified Monitor location of Wellington House. No data will flow to RNOH. Monitor will not use data for any commercial purpose. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff, PLICS, GIRFT data packs and Model Hospital Dashboard outputs. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Example outputs that will form part of the core functions set out in the purpose section are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, NHSI expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. The Mental Health dataset will generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. They will be used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). PLICS pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. Data are to be used to calculating metrics for dashboards and in particular for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications. HES CIP data will be used to develop the the Single Oversight Framework (SOF) for trusts. Data are also used to support Improvement initiatives across TDA such as taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data. Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, include: https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver	Benefits which will be achieved from having access to the data requested are; • Enabling the delivery of a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners • Development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff • Enabling a better more effective payment system which in turn would not just the users but all of the NHS • Enabling the development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. supporting regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level • The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020 Access to the data will also enable the TDA to deliver a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: • standards relating to quality of care • their duty to exercise their functions efficiently, economically and effectively • the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable • Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care • Single Operating Framework Output helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Standard Monthly Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. This application seeks to request data for both the TDA and Monitor as Data controllers in common. The TDA is a Special Health Authority established by Article 2 of the TDA (Establishment and Constitution) Order 2012. The NHS TDA is also made up of the Patient Safety, the National Reporting and Learning System, the Advancing Change and the Intensive Support Teams. Under the NHS DA (Directions and Miscellaneous Amendments etc) Regulations 2016 it has a general power to take such steps as it considers necessary and appropriate to assist and support persons providing NHS services to ensure continuous improvement in the quality of the provision and the financial sustainability of NHS services. Monitor is a statutory body. Under the Health and Social Care Act 2012. It has a duty when exercising its functions to protect and promote patient interests by promoting economic, efficient and effective health care services whilst maintaining or improving quality. Monitor must co-operate with Special Health Authorities including the NHS TDA. Monitor and the NHS Trust Development Authority (TDA) have come together under the operational name NHS Improvement, combining the functions and responsibilities of the 2 statutory bodies in a single integrated organisation. As such, NHS Improvement is responsible, among other things, for the oversight of NHS trusts, NHS foundation trusts and independent providers. Monitor require access to the following data sets as part of this request; Hospital Episode Statistics (HES) Mental Health Data Sets (MHMDS) (MHLDDS) (MHSDS) Secondary Uses Service Payment By results (SUS Pbr) Patient Reported Outcome Measures (PROMS) Diagnostic Imaging Data Set (DiDs) Patient Level Costing Data (PLICS) data will also be shared through this agreement for both Acute and Mental Health. The purposes for access are; (1) Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. And, promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) This includes; • The Costing Transformation Programme (CTP), was established to implement Patient Level Information Costing System (PLICS) across Acute, Mental Health, Ambulance and Community providers and. The programme entails: a. Introducing and implementing new standards for patient level costing. b. Developing and implementing one single national cost collection to replace current multiple collections; c. Establishing the minimum required standards for costing software and promoting its adoption; and d. Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve and develop products to help support service improvements within hospitals. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital and develop products to help support service improvements within hospitals. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. (2) Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Monitor has a statutory duty to publish the national tariff. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: a. health care services which are or may be provided for the purposes of the NHS b. the method used for determining national price c. the national price of each of those service d. the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices) e. the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price. (3) Monitor change their working pattern frequently as part of investigating future models/projects. Monitor uses HES and SUS PbR data to calculate the pricing analysis and improvement models. PROMS is also required for pricing analysis. PROMS will be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to assess the performance of trusts. Linked PROMS data will enable impact analysis of new outcome based payment models for in hospital services and therefore will assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. (4) Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • Assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers (5) Monitor will share the analysis and underlying data back with the trusts about whom the data pertains. Monitor will notify NHS Digital of each trust as and when a merger is being risk assessed by Monitor. Any such access/sharing of data would only take place where the provider has an existing DSA for HES data in place with NHS Digital. Before any access/sharing of analysis and data with trusts takes place, NHS Improvement will ensure that suitable controls are in place by reviewing the trusts security arrangements and entering into a DSA such that the HES data is used by the Trust solely in line with the purposes set out within the agreement. (6) Monitor requires the HES CIP as a metric calculation from NHS Digital and wish to use this as part of Monitor’s remit in developing the Single Oversight Framework (SOF) for trusts. Monitor are standardising their methodology in SOF to calculate re-admission metric as per national definition, which is to calculate readmissions from Continuous Inpatient Spells. The purpose of the SOF is to help identify where providers may benefit from, or require, improvement support, to meet the standards required of them in a safe and sustainable way. It sets out how NHSI identify providers’ potential support needs, and determines the way they work with each provider to ensure appropriate support is made available where required. There are a number of NHS Digital data sets used to develop metrics in the SOF, this is an additional metric to help measure Emergency readmissions within 30 days of discharge from hospital. NHS Trust Development Authority (NHS TDA) TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: (7) Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: • Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, Mental health data and SUS PbR. NHS Improvement and the Royal National Orthopaedic Hospital NHS Trust (RNOH) are working together to develop and expand the Getting it Right First Time Programme, which is a programme to improve the productivity, efficiency and quality of care of NHS providers. As part of that programme, RNOH wishes to analyse a wide range of data about NHS providers, including information about their operations, performance and costs in order to formulate appropriate metrics for benchmarking analysis and identify means of improvements to help shape discussions with hospital clinicians and managers, and help encourage the development of improvement plans for hospitals.	Mental Health data will be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. HES data will be used to help NHSI perform its role in helping trusts navigate the regulatory issues surrounding a transaction under the umbrella of NHSI and helping trusts to provide better care. To help trusts identify any possible competition issues with a proposed merger, NHSI undertakes analysis in line with the approach. Data on elective activity (outpatient and admitted patient) is central to this analysis. Using an NHSI developed improvement tool, it has developed an approach to process and analyse the data over the recent years and can conduct the analysis very efficiently. The development of an internal Monitor data extraction tool (‘HES Browser’) provides an opportunity to achieve the desired efficiency gains in this process. The tool (when fully operational) is expected to enable Monitor to process HES data efficiently and carry out the analysis quickly. However the data processed with HES Browser comes from NHSD and is subject to Monitor’s contract with NHSD. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES, SUS PbR, PROMS, and/or Mental Health data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at an aggregated level and with small numbers suppressed in line with the HES analysis guide. Building on the Acute PLICS pilot collections, Monitor is working with NHS Digital to establish a PLICS Mental Health system to successfully collect, collate, link, pseudonymise and validate data. The pilot shall look to establish clear mechanisms for safely transferring pseudonymised linked PLICS MHSDS data back to Monitor. A request made to NHS Digital under section 255 of the Health and Social Care Act, to establish and operate a system for the collection and analysis of PLICS mental health data was accepted on 6th September 2017. The pseudonymised MHSDS PLICS data will be held on the PLICS portal hosted on the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked MHSDS PLICS data and record level PLICS data to individual Trusts who participate in the PLICS mental health pilot. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked PLICS data and record level PLICS data to individual Trusts who participated in the PLICS pilot collections. PLICS data shall include non-sensitive, non-identifiable fields relating to that Trust. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. The use of linked PLICS data is also required for the development of GIRFT data packs, GIRFT national reports, and products to support the work of regional GIRFT and NHS I implementation teams. These plans, reports will be populated with metric values from the PLICS portal. Data may be extracted from the portal and provided back to the submitting Trust. In this case the following rules will apply: o The Trust must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle NHS Digital data. o The Trust will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The Trust will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital i.e. who were the original data owners. NHS Improvement will ensure that suitable controls are in place such that the data is used by the Trust solely in line with the purposes set out within the agreement. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor and the TDA will only publish analytical anonymous data with small numbers suppressed. Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development. Monitor and the NHS TDA (and their Data Processors) will not disseminate data in the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. Access to all data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or Royal National Orthopaedic Hospital. The Royal National Orthopaedic Hospital NHS Trust (RNOH), are included as a data processor however processing will only occur at the specified Monitor location of Wellington House. No data will flow to RNOH. Monitor will not use data for any commercial purpose. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff, PLICS, GIRFT data packs and Model Hospital Dashboard outputs. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Example outputs that will form part of the core functions set out in the purpose section are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, NHSI expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. The Mental Health dataset will generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. They will be used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). PLICS pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. Data are to be used to calculating metrics for dashboards and in particular for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications. HES CIP data will be used to develop the the Single Oversight Framework (SOF) for trusts. Data are also used to support Improvement initiatives across TDA such as taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data. Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, include: https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver	Benefits which will be achieved from having access to the data requested are; • Enabling the delivery of a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners • Development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff • Enabling a better more effective payment system which in turn would not just the users but all of the NHS • Enabling the development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. supporting regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level • The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020 Access to the data will also enable the TDA to deliver a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: • standards relating to quality of care • their duty to exercise their functions efficiently, economically and effectively • the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable • Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care • Single Operating Framework Output helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
MONITOR	MONITOR	Standard Monthly Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS Improvement (NHSI) was launched on 1 April 2016 and is the operational name for the organisation that brings together Monitor and the NHS Trust Development Authority (“TDA” plus a number of other teams). NHS Improvement operates as a single organisation, with a joint board and single leadership and operating model although the TDA and Monitor continue to exist as distinct legal entities with their continuing statutory functions, legal powers and staff. This application seeks to request data for both the TDA and Monitor as Data controllers in common. The TDA is a Special Health Authority established by Article 2 of the TDA (Establishment and Constitution) Order 2012. The NHS TDA is also made up of the Patient Safety, the National Reporting and Learning System, the Advancing Change and the Intensive Support Teams. Under the NHS DA (Directions and Miscellaneous Amendments etc) Regulations 2016 it has a general power to take such steps as it considers necessary and appropriate to assist and support persons providing NHS services to ensure continuous improvement in the quality of the provision and the financial sustainability of NHS services. Monitor is a statutory body. Under the Health and Social Care Act 2012. It has a duty when exercising its functions to protect and promote patient interests by promoting economic, efficient and effective health care services whilst maintaining or improving quality. Monitor must co-operate with Special Health Authorities including the NHS TDA. Monitor and the NHS Trust Development Authority (TDA) have come together under the operational name NHS Improvement, combining the functions and responsibilities of the 2 statutory bodies in a single integrated organisation. As such, NHS Improvement is responsible, among other things, for the oversight of NHS trusts, NHS foundation trusts and independent providers. Monitor require access to the following data sets as part of this request; Hospital Episode Statistics (HES) Mental Health Data Sets (MHMDS) (MHLDDS) (MHSDS) Secondary Uses Service Payment By results (SUS Pbr) Patient Reported Outcome Measures (PROMS) Diagnostic Imaging Data Set (DiDs) Patient Level Costing Data (PLICS) data will also be shared through this agreement for both Acute and Mental Health. The purposes for access are; (1) Licensing providers of NHS services in England (Part 3, Chapter 3 of the 2012 Act), in particular, ensuring that providers comply with the conditions of their license relating to continued provision of health care services for the purposes of the NHS. And, promoting the integration of care where this would improve the quality and efficacy of care and/or drive efficiencies (Part3, Chapter 1 of the 2012 Act) This includes; • The Costing Transformation Programme (CTP), was established to implement Patient Level Information Costing System (PLICS) across Acute, Mental Health, Ambulance and Community providers and. The programme entails: a. Introducing and implementing new standards for patient level costing. b. Developing and implementing one single national cost collection to replace current multiple collections; c. Establishing the minimum required standards for costing software and promoting its adoption; and d. Driving and encouraging sector support to adopt Patient Level Costing methodology and technology. • Developing the Carter Programme and the Model Hospital dashboard and metrics – a nationally available online information system, with a series of themed compartments which present key performance metrics for different area across the hospital, enabling providers to compare performance against their peers and national benchmarks, and identify areas where they need to improve and develop products to help support service improvements within hospitals. • Developing ‘The Getting It Right First Time Programme’ (GIRFT) - supporting and offering expertise to the NHS and elsewhere on the provision of surgical and medical hospital services. The GIRFT programme develops hospital level data packages to help encourage the development of improvement plans for each hospital and develop products to help support service improvements within hospitals. A national recommendation report is developed. Hospitals are expected to monitor the implementation of their improvement plans using data published on the ‘Model Hospital’ dashboard. • Studying how a failing provider's activity could be re-directed to other hospitals. (2) Developing, publishing and enforcing the national tariff (Part 3, Chapter 4 of the 2012 Act), which will include: • Monitor has a statutory duty to publish the national tariff. The National Tariff is produced in conjunction with NHS England. In order to comply with the statutory duty, Monitor needs access to Casemix HES patient level data to facilitate the development, quality assurance and monitoring of the national tariff system Policy. In particular the national tariff must specify: a. health care services which are or may be provided for the purposes of the NHS b. the method used for determining national price c. the national price of each of those service d. the method used for deciding whether to approve an agreement under section 124 and for determining an application under section 125 (local modifications of prices) e. the rules governing local variations to national prices and the rules governing local price setting arrangements where there is no national price. (3) Monitor change their working pattern frequently as part of investigating future models/projects. Monitor uses HES and SUS PbR data to calculate the pricing analysis and improvement models. PROMS is also required for pricing analysis. PROMS will be used for future design of Impact Assessment works and efficiency measures in which Monitor will be able to assess the performance of trusts. Linked PROMS data will enable impact analysis of new outcome based payment models for in hospital services and therefore will assist in the design and evaluation of suitability of partially outcome based payment as a part of the national payment system. PROMS will also be used to support the new payment system for Urgent and Emergency Care as this payment system is envisaged to have a link to patient outcomes. (4) Preventing anti-competitive behaviour by providers and commissioners NHS Procurement, Patient Choice and Competition Regulations 2013, in particular (Part 3, Chapter 2 of the Act): • Assessing activity in any given Local Health Economy to ensure that any competition in the health sector is fair and that it operates in the best interests of patients • Providing advice and guidance to NHS organisations who are considering mergers (5) Monitor will share the analysis and underlying data back with the trusts about whom the data pertains. Monitor will notify NHS Digital of each trust as and when a merger is being risk assessed by Monitor. Any such access/sharing of data would only take place where the provider has an existing DSA for HES data in place with NHS Digital. Before any access/sharing of analysis and data with trusts takes place, NHS Improvement will ensure that suitable controls are in place by reviewing the trusts security arrangements and entering into a DSA such that the HES data is used by the Trust solely in line with the purposes set out within the agreement. (6) Monitor requires the HES CIP as a metric calculation from NHS Digital and wish to use this as part of Monitor’s remit in developing the Single Oversight Framework (SOF) for trusts. Monitor are standardising their methodology in SOF to calculate re-admission metric as per national definition, which is to calculate readmissions from Continuous Inpatient Spells. The purpose of the SOF is to help identify where providers may benefit from, or require, improvement support, to meet the standards required of them in a safe and sustainable way. It sets out how NHSI identify providers’ potential support needs, and determines the way they work with each provider to ensure appropriate support is made available where required. There are a number of NHS Digital data sets used to develop metrics in the SOF, this is an additional metric to help measure Emergency readmissions within 30 days of discharge from hospital. NHS Trust Development Authority (NHS TDA) TDA requires access to HES, SUS PbR, PROMS, DIDs and Mental Health linked data collected over a number of years by NHS Digital to fulfil aspects of its role largely set out in the NHS Trust Development Authority Directions and Revocations and the Revocation of the Imperial College Healthcare NHS Trust Directions 2016, in particular its general functions in Part 2 relating to improvement in the health service and designing methods and publishing guidance; and its functions in Part 3 and 4 relating to overseeing NHS trusts and making appointments to their boards. This includes using the data for: (7) Ensuring that NHS trusts comply with their duty under section 26 of the NHS Act 2006 to exercise their functions efficiently, economically and effectively, and ensuring they comply with such conditions equivalent to the NHS provider licence as the TDA specifies including: • Supporting and developing the indicators in the Single Oversight Framework which are used to monitor the performance of Trusts. Indicators from HES include ,long average lengths of stay, high new to follow-up ratios and long waits at A&E, early identification of any problems to help NHS Improvement to highlight these issues with clinical and management staff in Trusts, and help to avert poor outcomes. • Supporting other work programmes including activity dashboards such as Systems Economics Dashboard, A&E, HES browser. • Other outputs are research, developmental work, statistical analyses in order to help offer support to providers. Ad hoc analyses carried out, would typically involve data sets such as HES, Mental health data and SUS PbR. NHS Improvement and the Royal National Orthopaedic Hospital NHS Trust (RNOH) are working together to develop and expand the Getting it Right First Time Programme, which is a programme to improve the productivity, efficiency and quality of care of NHS providers. As part of that programme, RNOH wishes to analyse a wide range of data about NHS providers, including information about their operations, performance and costs in order to formulate appropriate metrics for benchmarking analysis and identify means of improvements to help shape discussions with hospital clinicians and managers, and help encourage the development of improvement plans for hospitals.	Mental Health data will be used to develop Monitor’s mental health modules of analysis within Monitor’s Local Health Economy Intelligence Unit data packs. The data will undergo analytical tests to assess the interactions between mental health and acute care (acute care activity by patients with mental health conditions across all local health economies in England). The data will not be linked to any other datasets. Trend analyses may be created for other indicators, with named dashboards enabling comparison with sector peer groups. Ad hoc analyses are carried out where the regular outputs raise questions, or where analysis would assist TDA carry out required duties. HES data will be used to help NHSI perform its role in helping trusts navigate the regulatory issues surrounding a transaction under the umbrella of NHSI and helping trusts to provide better care. To help trusts identify any possible competition issues with a proposed merger, NHSI undertakes analysis in line with the approach. Data on elective activity (outpatient and admitted patient) is central to this analysis. Using an NHSI developed improvement tool, it has developed an approach to process and analyse the data over the recent years and can conduct the analysis very efficiently. The development of an internal Monitor data extraction tool (‘HES Browser’) provides an opportunity to achieve the desired efficiency gains in this process. The tool (when fully operational) is expected to enable Monitor to process HES data efficiently and carry out the analysis quickly. However the data processed with HES Browser comes from NHSD and is subject to Monitor’s contract with NHSD. For the purpose of the tariff production, Casemix HES data may be linked to patient level pseudonymised data specified in this agreement. For clarity, Casemix HES may be linked to HES, SUS PbR, PROMS, and/or Mental Health data at patient level. Other aggregated, non-identifiable datasets such as ODS, IMD, OPCS, ICD10 among others, will be analysed in combination with the Casemix HES data. The aggregated datasets will only be compared at an aggregated level and with small numbers suppressed in line with the HES analysis guide. Building on the Acute PLICS pilot collections, Monitor is working with NHS Digital to establish a PLICS Mental Health system to successfully collect, collate, link, pseudonymise and validate data. The pilot shall look to establish clear mechanisms for safely transferring pseudonymised linked PLICS MHSDS data back to Monitor. A request made to NHS Digital under section 255 of the Health and Social Care Act, to establish and operate a system for the collection and analysis of PLICS mental health data was accepted on 6th September 2017. The pseudonymised MHSDS PLICS data will be held on the PLICS portal hosted on the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked MHSDS PLICS data and record level PLICS data to individual Trusts who participate in the PLICS mental health pilot. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. NHS Improvement’s Operational Productivity team have begun aligning initial data outputs (generated as part of the initial PLICs pilot collection) with the Model Hospital dashboard. The dashboard shall provide aggregated small number suppressed summary of pseudonymised linked PLICS data and record level PLICS data to individual Trusts who participated in the PLICS pilot collections. PLICS data shall include non-sensitive, non-identifiable fields relating to that Trust. Trusts may also see aggregate data (with small numbers suppressed) for other organisations. The use of linked PLICS data is also required for the development of GIRFT data packs, GIRFT national reports, and products to support the work of regional GIRFT and NHS I implementation teams. These plans, reports will be populated with metric values from the PLICS portal. Data may be extracted from the portal and provided back to the submitting Trust. In this case the following rules will apply: o The Trust must have a separate DSA (Data Sharing Agreement) with NHS Digital to handle NHS Digital data. o The Trust will be provided with aggregate data (i.e. no patient-level data will be provided) but the data may include unsuppressed small numbers. The Trust will have the necessary approvals in place to handle unsuppressed small numbers from NHS Digital before any access to data is granted. One of the main points of the GIRFT work is to identify Trusts who are doing work at unsafe levels, so being able to show small numbers illustrates this more strongly than an <5 default code. Data would only be released with unsuppressed small numbers under a strict release protocol and only in data packs that are released to the Trust who submitted data to NHS Digital i.e. who were the original data owners. NHS Improvement will ensure that suitable controls are in place such that the data is used by the Trust solely in line with the purposes set out within the agreement. Monitor needs to be able to share the Casemix HES and Grouper Output data with NHS England for the purpose of developing the National Tariff only. The purpose of sharing the data with NHS England is to facilitate in the development of the national tariff. Both Monitor and NHS England have been mandated to produce this national tariff under the 2012 Act. Monitor will be sharing with NHS England the Casemix HES and Grouper Output data that have gone through a cleansing process including impact assessments used to determine the financial effects of these findings on the healthcare sector. This information Monitor will then share with NHS England who also conducts their own impact assessments. Separately, NHS England also receives the source Casemix/Grouper data from NHS Digital. The two sets of records are then used to determine and agree the national tariff. Only NHS England staff who are working on the national tariff are permitted to access the data included within this agreement. Monitor also requires the ability to share analysis derived from the Casemix HES data with NHS Digital. Aggregated and summarised data as well as the results of the analysis will ultimately be made public. Monitor and the TDA will only publish analytical anonymous data with small numbers suppressed. Results of the analysis may be shared prior to publication with colleagues at other NHS organisations to inform future policy development. Monitor and the NHS TDA (and their Data Processors) will not disseminate data in the format it is received, or any subset of the said data, to any third party not included in this agreement with the exception of the data to trusts via the GIRFT programme where data would only be shared when the necessary approvals and agreements are in place with NHS Digital. Access to all data will be restricted to people employed by or contracted to Monitor, NHS TDA, NHS England, or Royal National Orthopaedic Hospital. The Royal National Orthopaedic Hospital NHS Trust (RNOH), are included as a data processor however processing will only occur at the specified Monitor location of Wellington House. No data will flow to RNOH. Monitor will not use data for any commercial purpose. Monitor will retain the Intellectual Property Rights to any works derived from or including the production of the National Tariff, PLICS, GIRFT data packs and Model Hospital Dashboard outputs. For clarity, Monitor and NHS TDA will act as data controllers in common and joint data processors. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).	Example outputs that will form part of the core functions set out in the purpose section are: Developing the Carter Model Hospital and the GIRFT programme: • Calculating metrics for the Model Hospital dashboard • Calculating metrics for the hospital data packages and national recommendation reports, network or STP reports, ad hoc reports and peer-reviewed publications, under the following conditions: o The hospital data packages will only be published to the hospital from which the data was originally sourced (therefore, NHSI expect to show small numbers) o National recommendation reports will only include aggregate data. No individual hospital will be named, and no small numbers will be shown o The ‘Model Hospital’ dashboard will identify individual hospitals, and small numbers will be supressed o Network or STP reports, where data from more than one hospital are included and published to an audience that contains personnel from more than one NHS organisation, will identify individual hospitals, and small numbers will be supressed o Ad hoc reports for NHS managers or clinicians (e.g. NHS England, NHS Improvement, Royal College of Surgeons, etc.) will identify individual hospitals, and small numbers will be supressed o Articles in peer-reviewed publications will only include aggregate data. No individual hospital will be named, and no small numbers will be shown. - Reports on total tariff and activity by provider and commissioning body - Referral patterns from GP practices to trusts - Investigations of the effects of potential tariff changes on the health economy - Modelling life-years-of-care - Reporting activity by variable aggregations - Taking enforcement action in relation to any non-compliance identified from analysis of the data Monitor will process the data to set National Tariff Prices for FY 2016/17 and subsequent years. https://www.gov.uk/government/consultations/nhs-national-tariff-payment-system-201617-a-consultation This year’s national tariff proposals aim to give providers of NHS services the space to restore financial balance and support providers and commissioners to make ambitious longer term plans for their local health economies. These proposals will help providers and commissioners to work together to manage demand and deliver services more efficiently. This continues the development of the payment system for mental healthcare. The Mental Health dataset will generate informative slide(s) that capture the interactions of mental health patients with secondary acute services to provide contextual information within the LHE. They will be used to support regional monitoring teams facilitate discussions with their trusts during the monitoring process, and possible the regional Tripartite (if issues identified that should be addressed by the LHE). PLICS pilot implementation and collection has enabled the standards and approach to collection used in 2016 to be refined for 2017 to ensure that the approach is implementable, reducing the risk and burden on provider as far as possible. The collection in 2017 will encompass 80-90 providers, who are being supported by NHS Improvement to work towards implementing the standards, although it remains a voluntary collection at this stage. It is also worth noting that the 80-90 Trusts includes a subset of Trusts who will provide a representative sample of HRGs, to allow PLICS data collected to inform the development of the next tariff; one of the benefits of the move to PLICS being better quality cost data to inform NHSI’s Pricing functions. Data are to be used to calculating metrics for dashboards and in particular for the hospital data packages and national recommendation reports, network or Sustainability and Transformation (STP) reports, ad hoc reports and peer-reviewed publications. HES CIP data will be used to develop the the Single Oversight Framework (SOF) for trusts. Data are also used to support Improvement initiatives across TDA such as taking enforcement action against NHS trusts in relation to any non-compliance identified from analysis of the data. Some specific examples of outputs already produced, highlighting the range of analysis undertaken, and going some way to justify the need for such wide-reaching data, include: https://www.gov.uk/government/publications/making-local-health-economies-work-better-for-patients https://www.gov.uk/government/publications/five-year-forward-view-time-to-deliver	Benefits which will be achieved from having access to the data requested are; • Enabling the delivery of a better contextual view of provider performance, providing assurance that providers of health care are meeting the terms of their license, prevention of anti-competitive behaviour by providers and commissioners • Development of the national Tariff allowing providers of NHS care to be reimbursed for care provision according to the national tariff • Enabling a better more effective payment system which in turn would not just the users but all of the NHS • Enabling the development of a consistent and systematic analysis on the relationship between mental health care and acute secondary services across all LHE in England. supporting regional teams to monitor their Trust, against a broader macro-economic context of their local health economy, and the dynamics at play between mental and physical health at a local level • The benefits that the CMH (and GIRFT programme as part of the MH works and portal that will host the dashboards) will bring to the NHS are the offerings of mechanisms via the MH dashboards that can measure a provider’s productivity and efficiency and help them to reduce unwarranted variation in productivity and ultimately save the NHS £5billion each year by 2020 Access to the data will also enable the TDA to deliver a better contextual view of NHS provider performance, including providing assurance that NHS trusts are complying relevant standards and requirements including: • standards relating to quality of care • their duty to exercise their functions efficiently, economically and effectively • the requirements of the conditions equivalent to the NHS provider licence, which the TDA has specified as being applicable • Access and use of NHS Digital data is to support and guide Trusts in their provision of quality sustainable services or to find an alternative viable solution. Typically this will involve discussions and assessments by colleagues in regional development teams with managers in trusts, and, also at an executive level. It is intended that the information obtained via HES is used as a driver to improve patient care • Single Operating Framework Output helps to provide an understanding of what is happening in the sector and assess how well or badly a trust is performing. It is intended that Trusts will be able to make evidence based decisions to improve the outcomes for patients. HES data shall be used to assist in the analysis A&E performance. Many Trusts have been struggling to achieve the 95% target of completing treatment at A&E within 4 hours. The benefit of producing this analysis is that, by comparing the performance of Trusts across England, this will help to identify Trusts where there is scope for improvement with the intention ultimately of improving patient care.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Office for National Statistics Mortality Data	Identifiable	Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Improving Access to Psychological Therapies Data Set	Anonymised - ICO code compliant	Sensitive	Other-National Audit Act 1983	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery. The data will be used primarily but not exclusively to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to seven. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee. Data HES data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in audit work and helping the NHS to improve services, they are increasingly making use of HES data, and other data made available by the HSCIC, in order to provide insights and evidence. This HES refresh and previous data will be used to support in-depth and timely analysis to inform the reports that hold the Department accountable, and help the NHS and social care to improve services. The SUS PbR refresh and previous data will utilize the tariff data to support analysis on healthcare costs and expenditure that complements NAO audit functions, tariff data is currently unavailable within HES data. In analysing VFM, having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. For all the data requested, the objective to hold the Department of Health to account remains the same, as does the treatment of the data and information assurance policy. Data cannot be minimized (e.g. by data years or geographical location) as NAO need to have the full dataset available so that they can respond to correspondence within a month (which NAO are required to do). Additionally NAO retain the executive decision to further explore the topic of correspondence, which will be done if there is sufficient evidence to suggest risk to VFM or public money. It would therefore be impractical and would not meet the needs of NAO to apply for additional data, if needed, to address specific questions.	All individuals who will have access to the HSCIC data will be employed by the NAO. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. Data will be deleted from the laptop when laptop is taken out of NAO office. HSCIC data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For HSCIC data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of HSCIC data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to HSCIC data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. Apart from public access to NAO outputs, HSCIC, as part of the DH group will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by HSCIC, on some occasions, NAO may clear directly with relevant staff from HSCIC; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from HSCIC for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and HSCIC will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from HSCIC if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery. The data will be used primarily but not exclusively to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to seven. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee. Data HES data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in audit work and helping the NHS to improve services, they are increasingly making use of HES data, and other data made available by the HSCIC, in order to provide insights and evidence. This HES refresh and previous data will be used to support in-depth and timely analysis to inform the reports that hold the Department accountable, and help the NHS and social care to improve services. The SUS PbR refresh and previous data will utilize the tariff data to support analysis on healthcare costs and expenditure that complements NAO audit functions, tariff data is currently unavailable within HES data. In analysing VFM, having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. For all the data requested, the objective to hold the Department of Health to account remains the same, as does the treatment of the data and information assurance policy. Data cannot be minimized (e.g. by data years or geographical location) as NAO need to have the full dataset available so that they can respond to correspondence within a month (which NAO are required to do). Additionally NAO retain the executive decision to further explore the topic of correspondence, which will be done if there is sufficient evidence to suggest risk to VFM or public money. It would therefore be impractical and would not meet the needs of NAO to apply for additional data, if needed, to address specific questions.	All individuals who will have access to the HSCIC data will be employed by the NAO. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. Data will be deleted from the laptop when laptop is taken out of NAO office. HSCIC data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For HSCIC data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of HSCIC data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to HSCIC data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. Apart from public access to NAO outputs, HSCIC, as part of the DH group will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by HSCIC, on some occasions, NAO may clear directly with relevant staff from HSCIC; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from HSCIC for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and HSCIC will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from HSCIC if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery. The data will be used primarily but not exclusively to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to seven. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee. Data HES data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in audit work and helping the NHS to improve services, they are increasingly making use of HES data, and other data made available by the HSCIC, in order to provide insights and evidence. This HES refresh and previous data will be used to support in-depth and timely analysis to inform the reports that hold the Department accountable, and help the NHS and social care to improve services. The SUS PbR refresh and previous data will utilize the tariff data to support analysis on healthcare costs and expenditure that complements NAO audit functions, tariff data is currently unavailable within HES data. In analysing VFM, having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. For all the data requested, the objective to hold the Department of Health to account remains the same, as does the treatment of the data and information assurance policy. Data cannot be minimized (e.g. by data years or geographical location) as NAO need to have the full dataset available so that they can respond to correspondence within a month (which NAO are required to do). Additionally NAO retain the executive decision to further explore the topic of correspondence, which will be done if there is sufficient evidence to suggest risk to VFM or public money. It would therefore be impractical and would not meet the needs of NAO to apply for additional data, if needed, to address specific questions.	All individuals who will have access to the HSCIC data will be employed by the NAO. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. Data will be deleted from the laptop when laptop is taken out of NAO office. HSCIC data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For HSCIC data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of HSCIC data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to HSCIC data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. Apart from public access to NAO outputs, HSCIC, as part of the DH group will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by HSCIC, on some occasions, NAO may clear directly with relevant staff from HSCIC; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from HSCIC for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and HSCIC will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from HSCIC if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery. The data will be used primarily but not exclusively to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to seven. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee. Data HES data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in audit work and helping the NHS to improve services, they are increasingly making use of HES data, and other data made available by the HSCIC, in order to provide insights and evidence. This HES refresh and previous data will be used to support in-depth and timely analysis to inform the reports that hold the Department accountable, and help the NHS and social care to improve services. The SUS PbR refresh and previous data will utilize the tariff data to support analysis on healthcare costs and expenditure that complements NAO audit functions, tariff data is currently unavailable within HES data. In analysing VFM, having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. For all the data requested, the objective to hold the Department of Health to account remains the same, as does the treatment of the data and information assurance policy. Data cannot be minimized (e.g. by data years or geographical location) as NAO need to have the full dataset available so that they can respond to correspondence within a month (which NAO are required to do). Additionally NAO retain the executive decision to further explore the topic of correspondence, which will be done if there is sufficient evidence to suggest risk to VFM or public money. It would therefore be impractical and would not meet the needs of NAO to apply for additional data, if needed, to address specific questions.	All individuals who will have access to the HSCIC data will be employed by the NAO. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. Data will be deleted from the laptop when laptop is taken out of NAO office. HSCIC data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For HSCIC data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of HSCIC data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to HSCIC data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. Apart from public access to NAO outputs, HSCIC, as part of the DH group will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by HSCIC, on some occasions, NAO may clear directly with relevant staff from HSCIC; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from HSCIC for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and HSCIC will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from HSCIC if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL AUDIT OFFICE	NATIONAL AUDIT OFFICE	Improving Access to Psychological Therapies Data Set	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	The data will be used to focus on three areas that impact on all departments’ performance in achieving value for money (as defined by objectives one to three) as well as additional objectives four to eight. Objective One Improving financial management and reporting including production of Value For Money (VFM) reports Objective Two Making better use of information Objective Three Ensuring that services are delivered cost-effectively. Objective Four Providing responses to public enquiries and whistle blowing e.g. Public Accounts Committee, Health Select Committee and in some cases other Select Committees from Parliament. Objective Five Carrying out investigative work where there is risk to public money. Objective Six Generating insights into client bodies internally. Objective Seven Sharing insights with the Government including the Treasury and the Cabinet Office, local governments and NHS bodies to help them to improve services as required. Objective Eight Data will also be used to monitor the impact of the work and the progress made by the Government in improving services following recommendations from the Public Accounts Committee.	All individuals who will have access to the NHS Digital data will be employed by the NAO. They will either be substantive employees, or will be individuals, working under appropriate supervision on behalf of the NAO within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. Authorised individuals from the IT team will be responsible for maintaining the data on the server. Authorised individuals from study teams, including audit managers and analysts, will then either access and analyse the data through the server directly, or request IT to extract a subset of the data to analyse on their encrypted laptops only at the location specified. These laptops will not be removed from the NAO offices specified as processing/storage locations. NHS Digital data will be analysed by the analytical team, and in some cases, in combination with other datasets, using a range of analytical packages and statistical methods including regression analysis. The data included within this agreement will not be linked with any other data. Insights generated will be used to inform NAO’s views of and conclusions on the performance of the Government. The data requested do not include patient identifiable information for the linked ONS-HES data but will need to include: o Date of death: to enable NAO identify those episodes of care by patients at the end of life. o Age at death and cause of death: to help understand the trend in service utilisations by age profiles and type of mortalities. NAO have a body of data policies and IT standards, guidelines and procedures designed to ensure compliance with the Data Protection Act 1998. NAO take appropriate measures to safeguard the integrity and confidentiality of data they hold from unauthorised access. All staff and contractors have an obligation to comply with NAO data protection policies. Requests for personal data will be authorised by a senior employee, usually the Project Director. For most audits, the Project Director is the Information Asset Owner (IAO) and is personally responsible for authorising requests for personal data, and for ensuring that personal data is transferred, processed, stored and destroyed in accordance with NAO policies and procedures. For some audits an Engagement Director takes on these responsibilities and they provide assurances to the relevant IAO that they have complied with NAO policies and procedures. Analysis of the data is limited to the purposes set out in this application/agreement. The NAO complies with the Cabinet Office’s Security Policy Framework (SPF) and all NAO staff complete annual Information Risk training. For NHS Digital data, only selected NAO staff directly working on a particular project requiring evidence from the analysis of NHS Digital data are granted access on a case by case basis by a senior member of staff (Study manager or study lead) appointed by the Project Director and agreed with IT, other NAO staff who are not working on these projects will have no access to NHS Digital data held at NAO. Study manager or study lead report quarterly on the use and storage of these data to project directors. NAO ensure contractors working for National Audit Office at the NAO offices operate suitable procedures for personal data protection before they share data to them. These contractors are employed to support NAO in discharging their statutory and other audit responsibilities. NAO will receive full date of death from ONS data (all ONS data is subject to ONS terms and conditions). The data will help the NAO gain insights into, for example, the safety of hospital care, and service utilisation by all patients towards the end of their life. The full date of death data will allow NAO to understand the demand for/utilisation of services of patients in their last 1-2 years of life and evaluate the value for money of end-of-life hospital care, for example: - The patterns of services use towards the patients’ end of life; - whether and how they are different than the rest of life; and - how they have changed over the last 10-20 years; NAO also want to look at certain expensive interventions (like chemotherapy or big surgeries) towards the end of patients’ lives. This would allow NAO to analyse the effectiveness of such interventions in prolonging patient’s lives as well as gaining insights into the wider value for money of such interventions/government policies on those interventions. Due to the responsibilities of the NAO to respond quickly to Parliament, and due to the diverse range of topics for analysis, it is necessary to hold a number of years of data covering the whole population.	Outputs will only contain aggregate level data with small numbers suppressed in line with the HES Analysis Guide. Data may be used to provide insights only or to provide evidence, in that case, only data at aggregated level, for example, by geographic area or demographic characteristics, will be disclosed. Descriptive data and other analytical outputs may be reported as evidence in published reports. Some analysis and insights generated will be shared with NHS and other health and social care bodies to help them to improve services. Record-level data will be not be commented on or published. Client bodies (the Department of Health, its Arms-length bodies, the NHS and other relevant bodies funded by public money) which are audited on behalf of Parliament may have access to outputs of the analysis with the data but will not be granted access to the datasets that are held. The linked ONS mortality data will be used to inform a range of the outputs. These outputs include VFM reports, investigations, replies to correspondence and a range of other outputs at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle blowing. For example, some of the planned VFM/investigation work require examining the trend in demand for health care by people towards the end of their life. The linked HES-ONS mortality data will help NAO examine the trends and variations in service utilisations (for example, by geographic location or by NHS providers) by people towards the end of their life. For previous evidence of VFM studies and other reports, see http://www.nao.org.uk/search/type/report/sector/health-and-social-care Value for Money In general, the Health VFM team will produce approximately seven VFM reports per year to Parliament on the economy, efficiency and effectiveness with which the Department of Health and its constituent bodies use public money. The actual number of reports, the subject covered and the timing of the reports will be agreed with the Public Accounts Committee on an annual basis and may be subject to change at short notice based on current events, timing with other studies and PAC demand, and will vary from year to year. For this reason it is difficult to disclose specific study topics, though all studies will fall under the guidelines outlined here. A typical VFM study will last between 3-12 months but can be longer depending on the subject. A typical VFM study will include findings (and evidence to support these findings) on the performance by Government Departments and other public bodies, a conclusion on whether VFM has been achieved and recommendations for the Department in order to improve services. The target date for a given study depends on when it begins, which can be at any point during the year when Parliament is sitting. Other objectives A range of other outputs are produced at the request of Health Select Committee and other parliamentary committees, and additionally respond to public enquires including whistle-blowing. Investigative work is carried out if there a risk to public money or suspicions of wrongdoing within central government. Actual content, timing and format of these outputs will vary and can be unpredictable, but the objective will be the same, to hold the Government and other public bodies accountable and to help them to improve services. Support is provided to Health Select Committee and other smaller investigatory pieces of work conducted. This is triggered by correspondence from committee members, with other work typically triggered by correspondence from other concerned MPs and members of the public. It is expected on average to produce four pieces over a given year, but this depends on if and when NAO is contacted. Outputs will typically be a shorter piece of research and without a VFM conclusion, but will otherwise follow a similar format to the VFM studies. Again, there are clear restrictions in using individual level data. All NAO published reports will be publicly available on the website http://www.nao.org.uk under reports by sector. If the evidence used in the report is based on analysis using HES data, either from NAO analysis of record level data or HES online summary, NAO will clearly attribute this to the source, including who carried out the analysis. In 2016 the NAO published 11 reports into health and social care on the website, covering topics from social care, staffing, commissioning, discharge of older patients, treatment of vulnerable people in immigration centres, recovering costs for overseas patients, and finances more generally. So far in 2017 there have been 7 reports published on the website, covering mental health, learning disability, access to primary care, demand for ambulances, the backlog of unprocessed clinical correspondence, and the integration of health and social care (Better Care Fund). Apart from public access to NAO outputs, NHS Digital (as part of the DH group) will have access to NAO outputs in different ways at different stages of the NAO report. Depending on the nature of the report and the main body being audited, relevant outputs from analysis based on data provided by NHS Digital, on some occasions, NAO may clear directly with relevant staff from NHS Digital; on other occasions, NAO may clear the analysis with Department of Health or NHS England and other bodies from DH group, who in turn may seek advice from NHS Digital for the accuracy of NAO analysis. NAO analysis will always be cleared with the Department and other relevant bodies in the DH group. If the outcomes from NAO analysis of HES data are used for intelligence only or for discussions with the Department of Health and their constituent bodies, then these information will not be published for public access and NHS Digital will not usually have access to these outputs (unless HSCIC is involved directly in the discussion). However, NAO will always seek advice from NHS Digital if unsure about certain data items or the methods used in NAO analysis. Studies cannot be confirmed until they have been cleared by the Comptroller and Auditor General and the Public Accounts Committee at a reasonable point after initial development on the study has begun. NAO are therefore unable to provide a list and ETA for future studies. For all studies in progress the information will be made available on the NAO website under the work in progress page: http://www.nao.org.uk/work-in-progress/	The National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public service managers improve performance and service delivery on the subjects reported. The data will be used primarily, but not exclusively, to hold the Department of Health accountable, but other bodies may also be relevant including DCLG and Local Government Authorities, DWP and Department of Education. Based on the report and witnesses taken from Accounting Officers of the audited body, the Public Accounts Committee will produce their own recommendations to improve services which these bodies must respond to, usually in the form of Treasury Minutes. NAO monitors the progress made to the recommendations on behalf of the parliament. For any given study, NAO typically follow up on the progress of the department within a couple of years. NAO also works collaboratively during their work with these client bodies, sharing insights and perspectives with a range of Health and Social Care bodies, to help them improve financial management, service quality and sustainability, patient experience, efficiency and effectiveness. NAO’s work has continuously had positive impact on a range of health and social care services. HSCIC data has been an important source of evidence in the work of the NAO over the last few years allowing NAO to be more responsive and effective in their audit work, to support in-depth and timely analysis to inform the reports that hold the Department accountable, and to provide insights and evidence to help the NHS and social care to improve services. For example, SUS PbR data includes tariff data which supports analysis on healthcare costs and expenditure that complements NAO audit functions. Having an accurate picture of the money spent providing care is essential in making robust VFM conclusions. The data is essential to allow NAO to deal with concerns raised by the public and MPs, to provide information and evidence to a range of outputs, and to inform the Public Accounts Committee with independent information on more up-to-date development on an on-going basis.
NATIONAL CENTRE FOR SOCIAL RESEARCH	NATIONAL CENTRE FOR SOCIAL RESEARCH	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	NatCen collects permission from respondents for data linkage to Hospital Episode Statistics (HES), Personal Demographics Service (PDS), Cancer Registrations and ONS Mortality data on the English Longitudinal Study of Ageing (ELSA). They also request the same data for the Health Survey of England (HSE), but an application to link data to the HSE has been submitted separately. Being able to link the survey data to the administrative data is an important feature of the survey, because it allows association between the health and lifestyle information and measurements collected in the survey at a particular time with mortality, cancer and HES data over a longer time period. This application is to keep data already supplied by the HSCIC and to request further data in order to maintain and further develop the ELSA databases. A request to be able to share the linked data with 3rd parties via sub licensing agreements will made via an amendment application to HSCIC via DAAG in the near future. There would be no standard outputs, but all would be for the purposes of research. Requests might relate to whether people have died, age at death, cause of death, frequency of hospital episodes in total or for particular diagnoses, etc – see below. The linked data required, and the purpose for which it is required, would be set out in the Data Release Form submitted to NatCen. NatCen collects the survey data and acts as the data controller for providing access to the survey data (and in the future any associated linked data) to researchers who will use the data for specific research projects. The purpose of requesting linked data is to provide important information relating to the health of respondents. This includes details which would be too burdensome to ask them to provide in an interview. It also allows NatCen to understand the health of ELSA respondents who have not been able to continue with the study owing to ill-health. Also to create a rich dataset by linking HES, PDS, Cancer Registrations and ONS Mortality to the survey data for planned onward sharing with researchers. Linkage with HSCIC data is needed for many analyses to investigate the relationship between health conditions and behavioural and social characteristics reported in the survey and subsequent mortality. For instance, the linked data has been used to explore relationships between survey data on social class and raised blood pressure and mortality rates from coronary heart disease; the analyses also took into account demographic characteristics and other risk factors, such as smoking, family history of heart disease and cholesterol levels. ELSA is a continuation of HSE, not only being based on the HSE sample of older people, but also in having considerable overlap in the type of health information collected such that, for some analyses, the HSE interview is being treated as the baseline (wave 0). The two datasets, however, are never linked. There are 5 forms of usage that the researchers wish to address. 1. The maintenance and development of the ELSA database to be of the most benefit for health and social care 2. Administrative use when issuing the sample to interviewers, to avoid upsetting bereaved individuals by being prepared in advance 3. For calculation of response rates - NatCen need to know who has become ineligible because of death; the type of attrition is also important in understanding the study sample and the interpretations they can draw 4. For future 3rd party users more generally to have access to mortality information. Many longitudinal analyses will be completely misleading unless they can take account of censorship through death. For this the minimum needed is the month and year of death. This will be applied for separately 5. The interview data are deposited with the UK Data Service, a condition of ELSA funding. Linked data will be applied for separately	The requested data from the HSCIC would be linked to the survey data from the English Longitudinal Survey of Ageing (ELSA) . The linking process consists of matching the variables provided by HSCIC to the survey data via unique identifiers, termed ‘serial numbers’, for each participant who has consented to linkage. NatCen creates a new set of serial numbers for the linked data, and maintains a look up file which allows this to be linked to the survey data. The ELSA dataset and HSE dataset are not linked together and are linked to HSCIC separately and via separate agreements. Simple data flow: • NatCen provide NHS number, DOB, sex, postcode and ELSA member number to HSCIC • HSCIC provides NHS number, latest demographic data, Exits/re-entries to NHS, ONS mortality data and cancer registration data and pseudo HES data • No onward sharing is being requested in this application.	The primary objective of ELSA is to collect longitudinal data on health, disability, economics, and social participation and networks, from a broad-based sample of the English population aged 50 and older. This includes a unique coverage of biomedical, genetic, performance and psychosocial measures. Participants are approached biennially for the main interview. All waves included an interviewer-administered questionnaire and self-completion form; in waves 2, 4 and 6 there was also a nurse visit for biomedical measures. ELSA is not only a study of health but encompasses many facets of ageing. The focus of the study is to provide data necessary for an exploration of the unfolding dynamic relationships between health and functioning, social networks and economic position, as people plan for, move into and progress beyond retirement. It therefore has extensive components measuring financial status and social participation as well as health and cognition. Some of the questions that ELSA can address are: the nature and timing of retirement and post retirement labour market activity; the determinants of economic well-being at older ages; cognitive functioning and its impact on decision making among older people; disability and the compression of morbidity; the evolution of economic, social and health inequalities in an ageing population; social participation and social productivity at older ages; the impact of good quality health care on future health and well-being. The main output from this data request would be the creation of a rich and extremely useful ELSA database to be used as a resource for research analysis. Future outputs would include those described as collaborators (who are applying to HSCIC separately) and the planned sub licencing agreements to bona fide 3rd party researchers.	The primary objective of ELSA is to collect longitudinal data on health, disability, economics, and social participation and networks, from a broad-based sample of the English population aged 50 and older. This includes a unique coverage of biomedical, genetic, performance and psychosocial measures. ELSA is not only a study of health but it produces a great number of analyses and outputs relevant to or aimed at improving the provision of health or adult social care, or the promotion of health. ELSA plays an important role in providing high quality data from a multidisciplinary perspective that integrates information about the economic, social, psychological, community and health experience of older people in England. The ELSA research team publish a comprehensive report analysing each new wave of data that is collected. These are available to download from the study website - http://www.elsa-project.ac.uk/ - which itself hosts a wide range of information about the study and its findings. Of particular note is ELSA’s publication list, maintained on the website: http://www.elsa-project.ac.uk/publications . This provides a comprehensive listing of 250 outputs which have included findings based on analysis of ELSA. These include journal articles, working papers, book chapters and conference papers/presentations. Some examples relevant to the provision of health or adult social care or the promotion of health are included below: • Sexual health and well-being among older men and women in England: findings from the English Longitudinal Study of Ageing, David Lee , James Nazroo , Daryl O'Connor , Margaret Blake and Neil Pendleton , Archives of Sexual Behavior , Epub ahead of print] , January 2015 • Taking up physical activity in later life and healthy ageing: the English longitudinal study of ageing., Mark Hamer , Kim Lavoie and Simon Bacon , British Journal Of Sports Medicine , Vol: 48 (3), pp:239-43 , February 2014 • Limited health literacy is a barrier to colorectal cancer screening in England: Evidence from the English Longitudinal Study of Ageing., Lindsay C. Kobayashi , Jane Wardle and Christian von Wagner , Preventative medicine , November 2013 Journal Articles • The SES health gradient on both sides of the Atlantic, ELSA Working Paper, James Banks , Michael Marmot , Zoë Oldfield and James Smith , January 2007 • Association between low functional health literacy and mortality in older adults: longitudinal cohort study, Sophie Bostock and Andrew Steptoe , British Medical Journal , doi:10.1136/bmj.e.1602 , March 2012 Journal Articles The ELSA survey dataset is also a key output. Similar to HSE, ELSA survey data can be accessed by bonafide researchers who register to use with the UK data archive and download data sets to do their own analysis. Selected summary tables are also made available via the study website in Excel format - http://www.elsa-project.ac.uk/data_elsa. There is considerable use of the data in these ways. Like HSE, ELSA findings are used widely by those involved in the development of health and social care policy. As a result, questionnaire and related study content (such as the particular objective measurements used) are adjusted at each wave in response to policy concerns and to emerging social and health issues. For example, wave 7 included questions on hearing, oral health, extended questions on cognitive function and new questions on expectations and perceptions of the costs of social care. Selected examples of the benefits to improving the provision of health or adult social care, or the promotion of health which ELSA data can or has provided are included below: • Consequences of improved survival with serious illness and the rise in chronic disease. The longitudinal nature of ELSA makes it possible to monitor the experience of people as they acquire chronic illnesses, and evaluate the consequences of ill-health from economic, social and well-being perspectives. New measures of cognitive function included at waves 7 and 8 will allow us to estimate the prevalence of mild cognitive impairment and dementia throughout the country, as well as investigate their determinants and consequences for individuals and their families. We can use the detailed data on health, disability and functioning to monitor progress towards achievement of extended healthy life years. This information will also be relevant for government to inform the debate on key issues include the importance of improving care provision for the elderly, managing dementia more effectively through better treatment and research, and deciding how to pay for social care. • Social care. ELSA provides data permitting an understanding of the impact of changes in the range of social services available to older people on their well-being, health and social integration. Developing this understanding requires the collection of robust data that not only covers all relevant features of the care received, but also covers in detail the characteristics of those receiving, and not receiving, care, and that measures short, medium and long-term outcomes. The multidisciplinary and longitudinal nature of ELSA means that it has great potential in this regard. Data from the survey’s questionnaire module on social care can be used to address questions such as: older people’s receipt of and payment for care; the pattern of take-up of Direct Payments and Personal Budgets by older people; the provision of informal care; and the relationships between receipt of formal care, informal care and care needs among older people. • Public health and transport policy. ELSA data was used to explore the link between the introduction of a bus pass allowing free local travel during off-peak hours for those aged 60 or over and public health. The findings showed that the policy was associated with increased use of public transport and also that older people who used public transport were less likely to be obese, as were those eligible for free local bus travel. The results were noted by members of the Parliamentary Select Committees for Health and Transport, as well as policymakers at the Department for Transport, for whom they are valuable in future planning of concessionary bus travel policy. • Organisational changes in the NHS. The health data collected in ELSA, particularly when coupled with linked administrative data on healthcare, will provide important information about the use of services by the elderly before and after the transition from PCT to GP commissioning of care, the quality of health care, the interface between primary and secondary case, and the identification of trends in health that will impact on future demand. • Social isolation and loneliness. ELSA has provided valuable information about the relationship of social isolation and loneliness to well-being, health and cognition. The survey is an important resource for monitoring these experiences as people age. • Subjective well-being and public policy. ELSA includes measures of the different elements of subjective well-being since its inception, and these have been supplemented by the core questions from the ONS experimental module. It provides unique information about the trajectories of well-being among older people in England, and relationships with economic position, social factors, health, and cognition. The creation of these linked databases will provides further benefits in the near future when NATCEN requests that they be allowed to offer psuedonymised data via sub licence to 3rd party researchers. Each researcher would bring different benefits to health and social care, but by way of example the following are wishing to use linked ELSA /HSCIC (they are currently applying for the linked data separately with the HSCIC) Imperial - have been funded by DH to do a 2 year project on the potential health and economic benefits of the free bus pass, and the ELSA-linked data is the central part of this. The linked data will allow the researcher to examine whether there are any differences in hospitalisation/mortality associated with the bus pass, and this information will be the main driver of the economic modelling (and a lot of the policy discussion focuses on costs to benefits). DH are expecting the report in about a year’s time. University of East Anglia - The ELSA analysis is part of a larger programme of research which aims to develop an intervention to improve access to primary care for older people and test it within a feasibility study. The ELSA linked with HES analysis is vital because it will map risk factors along the patient pathway from recognising problems, accessing primary care and use of secondary care. The analysis will also be used to test concepts which come out of the interviews and literature review. Results from the ELSA linked with HES analysis will be triangulated with the other components to develop a new intervention to improve access to primary care for deprived older people in rural areas which will in turn should improve community healthcare
NATIONAL CJD SURVEILLANCE UNIT	NATIONAL CJD SURVEILLANCE UNIT	MRIS - Cause of Death Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to National CJD Surveillance Unit will be used only for the approved Medical Research Project - Transfusion Medicine Epidemiology Review
NATIONAL CJD SURVEILLANCE UNIT	NATIONAL CJD SURVEILLANCE UNIT	MRIS - Cohort Event Notification Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to National CJD Surveillance Unit will be used only for the approved Medical Research Project - Transfusion Medicine Epidemiology Review
NATIONAL INSTITUTE FOR CARDIOVASCULAR OUTCOMES RESEARCH	NATIONAL INSTITUTE FOR CARDIOVASCULAR OUTCOMES RESEARCH	Office for National Statistics Mortality Data	Identifiable	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	One-Off	Y	The processing is to enable NICOR to undertake its HQIP contracted audit work - reports and aggregate table reports at the unit or consultant level. Specifically to enable the delivery, by NICOR staff, of the National Cardiovascular Audit Programme consisting of the 6 following audits • Myocardial Ischaemia National Audit (MINAP) • Adult Cardiac Surgery Audit • National Heart Failure Audit • Congenital Heart Disease Audit, • Cardiac Rhythm Management Audit • Adult Cardiac Interventions Audit as contracted between NICOR and HQIP. The data may also be used to enable the delivery of additional audit analysis, with the approval of HQIP and as requested by those being audited (NHS Units and associated Consultants). Make the comparisons of disease/treatment outcome by hospital, or unit within a hospital and in some cases by healthcare professional. The outcome can be death or other recognized measure of care as deliver based upon national guidelines - treatment with a certain medication for instance. Where the data show performance is not as expected we work to agreed NHS rules about how the matter is investigated and quality improvement is made. This work on cardiovascular disease is part of a larger programme of use of data to ensure the NHS delivers the care it should to each and every patient. The results of all the analysis are made available on public facing websites- NICOR, NHSChoices, MyNHS as well as on the websites of the audit associated Professional Societies. The NICOR website highlights the latest report but allows access to previous reports and the NCHDA live portal lists the sources of where HQIP audit data is published.	NICOR (National Institute for Cardiovascular Outcomes Research) is part of the National Centre for Cardiovascular Preventions and Outcomes (NCCPO), which sits within the Institute of Cardiovascular Science (ICS) at University College London (UCL). Processing by NHS Digital of the cardiovascular audit data is required with both HES and ONS Mortality data, as done previously. Both HES and ONS data will be linked systematically by NHS Digital to the patient records submitted by hospitals/units, for each of the 6 audits using a number of variables (NHS Number, ID Number, Surname, Forename, Date of Birth, Gender, Postcode). NICOR will provide these patient identifiers to NHS Digital for linkage purposes. NHS Digital return to NICOR the linked HES and ONS data (fields detailed elsewhere). Before the linked data is used NICOR remove all patient identifiable fields so that the final dataset will be pseudonymised before the audit work is undertaken. No variables which might identify individuals (PID) will ever be published, reported or shared with a third party. Such analysis will only contain aggregated small numbers suppressed data in line with the HES Analysis guide. The audit data is processed to give, using all the available data held in the audits and linked mortality data, a full picture of each patients (anonymised) hospital based cardiac care. Other data from the audits is used to ensure that “risk” is taken into account on the basis of age and comorbidities. HES data is processed separately to enable an understanding of the completeness of the audit by comparison of patient numbers recorded between HES and the audit. All activities remain the same as in the previous application. NICOR are prohibited from sharing data, at a record level including any derived date , disseminated by NHS Digital to any 3rd parties. Only substantive employees of NICOR may process the data disseminated by NHS Digital and only for the purposes described in this agreement. The standard conditions for access to ONS data will be adhered to.	The outputs will be audit reports (in various formats) which will be published throughout 2016/17. The audit data and linked mortality data are used to generate scores of how each hospitals/ units/health professionals are performing compared to the national average for outcomes which have set guidelines for treatment. The outputs from the analysis are available to the hospitals and patients/general public via public facing NHS websites. This is presented in a graphic manner that can easily be understood by patients and the public. This helps patients, their family and the public know they are getting “best care” or where, by comparison, best care can be obtained. Hospitals/units and professionals who are the subject of the audits are expected to put Quality Improvement measures in place to ensure they are providing care at the expected level. These analyses and their publication are done on an annual basis. it is the intention to provide more regular reporting over the coming years. During the year 2016 all 6 audits published reports covering the years 2014/2015. During the period to end June 2017 the following audits will be reporting: Adult Cardiac Surgery (ACS) 2nd week June 2017 covering the period 2015-2016. Output as both hospital and consultant level hospital based mortality adjusted for risk score with reporting of outlier Alerts at 2 standard deviations from the norm and Alarms at 3 standard deviations. The output data is presented as a funnel plot. Alerts and alarms are reported to the medical director of each audited hospital. Alarms are also reported to HQIP who make onward reports to CQC and the GMC as required of the rules of the audit. The overall analysis will also show some centres as being positive outliers and this can be seen on the funnel plot. Myocardial Infarction (MINAP) last week April 2017 covering the period 2015-2016. This report will detail process measures in a comparative manner enabling comparison of hospitals and adherence to NICE Guidelines for the journey of care and times within that pathway as well as treatments given. There will also be a MINAP outcomes report 2014-2016 that is likely to be published at the end of June 2017. Heart Failure (HF) Expected end June (final date not yet agreed) covering 2015-2016. The output is by hospital on process matters detailing for example- Access to specialist staff; access to medicines; both as set out in NICE Guidelines/ standards. An aggregate mortality at both 30 days and one year is reported. Congenital Audit (NCHDA) reporting end of June 2017 covering period 2015-2016. This details activity numbers and 30 day mortality using the same Alert and Alarm basis as the ACS audit (detailed above)	There are a number of expected benefits: 1. The ability to look at cardiovascular admissions which may be related to, and impacted on by, the medical management of a patient’s heart failure. This will provide a much more detailed and complex picture of readmissions, and help us to determine the full impact that good and poor management of specific cardiac conditions has on readmission rates and mortality outcomes. 2. The ability to utilise readmission for reasons other than, but connected to, major cardiac surgery as an outcome measure would be extremely beneficial in terms of assessing the long term effects on patients undergoing the various cardiac surgical procedures, and what effect different variables have on these outcomes. 3. Provide additional insight into outcomes (especially adverse reactions such as stroke) which we can then include these in our annual reports used to inform quality improvement work. Linkage to the full HES dataset would allow further exploration of the geographic, socio-economic and organisational data of patients more detail. This could lead to a better understanding of commissioning patterns within the UK. In addition, the HES dataset collects information on augmented care and the patient care pathway. 4. The ability to investigate cumulative missed opportunities for patient care and major cardiovascular and cerebrovascular events. 5. The ability to determine case ascertainment rates and underreporting of procedures and patient admissions. These types of outputs will be included in the various publications NICOR produces including annual and other public reports (in various formats) for the key stakeholders such as clinicians, trusts, commissioners and patients. The information in the reports will be useful for Quality Improvement purposes. Each report contains specific targeted recommendations for its audience- Commissioners, Trust CEOs, Medical Directors and patients. The trust, where a negative outlier, is expected to put QI measures in place to bring care back to that expected. Cardiovascular disease is a major cause of morbidity and preventable mortality and when managed to agreed guidelines patients get the expected outcomes. The audits show how every hospital/unit and in some cases healthcare professionals in the NHS is preforming and where it is less than expected that Quality Improvement takes place. Patients can also see from the published information how their local hospital (s) are performing and have confidence in the decisions being made about their care and or be an active part of the decisions about where their hospital care will be undertaken. Each audit with outcome measures (ACS, NCHDA and HF hospitals and for ACS also) consultants will be expected to undertake Quality improvement measures that will bring their mortality and or other reported outcome and processes in line with that expected. These measures will be documented and have agreed timing plans. In summary the benefits to patients, the NHS and healthcare professionals is that healthcare is monitored to deliver at the Guideline level. All the outcomes data are presented in a manner understandable by patients so providing transparency of best, norm and below the norm centres.
NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Institute for Health and Care Excellence (NICE) is the independent organisation responsible for providing evidence-based guidance on health and social care. NICE guidance, standards and other resources help health, public health and social care professionals deliver the best possible care within the resources available. Hospital Episode Statistics (HES) are used for the following: - during the scoping of guidance and standards; - at review; and - to develop resource impact assessment tools for commissioners/and providers. The data is used to evaluate activity both prior to and following publication of guidance/standards e.g. when developing guidance or deciding if it should be reviewed. This data allows NICE to establish current activity levels in the NHS and enables the resource impact of implementing guidance to be established with increased precision. NICE uses HES data during the development of general practice and CCG level indicators. This is work jointly completed by NICE and NHS Digital on behalf of NHS England. These indicators provide clear, comparative information about the quality of health services and may be included in national frameworks such as the QOF, CCGOIS and CCGIAF. Extract data at a national level but may need local level data for highly specialised technologies (small numbers of people) or for work associated with the cancer drugs fund. Data will only be used for purposes relating to the provision of healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	The data will be extracted from HDIS in an aggregated form and shared with guidance, guideline, indicator and tool developing teams within NICE. The data is shared using a secure distribution method (NICE Share is a HTTPS file sharing application based on Microsoft IIS. All communication is encrypted using HTTPS and authentication is required for any file access). The data will be used to: assess current activity levels in the NHS, in cost effectiveness analysis (economic modelling) and to cost the implementation (resource impact assessment) of NICE guidance. All data extracted from HDIS which includes small number are stored in network folders which have restricted access. The HDIS database and any extracted aggregate data which includes small numbers will only be access using the NICE network through desktop PCs. Any data held temporarily on portable equipment must be supported by a business case and must be encrypted to a minimum of AES25. To complete trend analysis NICE require access to the last five years of data. Analysis of the last full five years data enables patterns and trends to be established and allows NICE to assess the level of need/cost with increased precision. The data extracted from HDIS is compared to other datasets at a national level (England) e.g. prescribing data in the Innovation Scorecard. No data will be linked to record level patient data. The majority of the data extracted from HDIS is at a national level (England) however access to local level data (CCG or trust) is sometimes needed when developing resource impact assessment tools or during the scoping of guidance to establish if there is variation in practice across the country (England). Access to pseudonymised record level data is essential when considering guidance for rare conditions/diseases, as they often affect very small numbers of people. NICE has offices in Manchester and London, and the data extracted from HDIS will be stored and accessed using servers based onsite in these locations; each server is a mirror of the other. The security arrangements and policies are the same for both locations. HDIS is only accessed by Manchester based staff. Record level data will not be extracted from the database. All extracts taken from the database will be aggregate level which may contain small numbers. Data in format suitable for publication, after the small numbers policy has been applied (in line with the HES analysis guide) are shared with committee members. All committee members have signed confidentiality agreements. Outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide.	The data is being used for the development of guidance/standards that: are independent and authoritative, are based on the best available evidence and set out the best ways to prevent, diagnose and treat disease and ill health, promote healthy living, and care for vulnerable people. The National Institute for Health and Care Excellence (NICE) provides national guidance and advice to improve health and social care. HES data are used during guidance development and to produce tools to support its implementation. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any outputs that are produced from the system that are to be published or shared will be small number suppressed outputs in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. The results are provided to teams within NICE responsible for guidance/standard/indicator development. They are also discussed by guidance/indicator developing committees. Key documents used during the development of guidance/standards are published on NICE’s website. Only data in a format suitable for publication will be presented. These documents are available free of charge to the public. Outputs will contain only aggregate level of data and small numbers suppressed in line with the HES analysis guide/ small numbers policy. NICE publishes new guidance throughout the year and so this work is on-going. HES data is also used by NICE’s indicator development team during the development phase of QOF and CCG OIS indicators. This is work jointly undertaken by NICE and the NHS Digital, and is funded by NHS England. The data are discussed at indicator development committees but only once the small numbers policy has been applied (see above).	Evidence based guidance has clear benefits for the Health and Social Care sectors and for patients. The benefits of using the data extend beyond the term of the proposed contract. The uptake and impact of NICE guidance can be found here: https://www.nice.org.uk/About/What-we-do/Into-practice/Measuring-the-uptake-of-NICE-guidance. Measuring the impact of NICE guidance is complicated as many other factors also contribute to patient outcomes. Examples of benefits achieved using HDIS NICE uses HDIS HES data to support the development and implementation of NICE Guidance and Quality Standards in the following ways:- HES data are used to provide the NHS with information to help them understand the resource impact of implementing NICE guidance. NICE also uses the data to develop evidence based guidance. The data are used to understand the population likely to benefit from the introduction of new medicines and technologies. The following are two specific examples to illustrate how NICE uses this data: 1. Use of data to support implementation tools (NICE guidelines [NG59]) Output: Resource impact statement published November 2016 https://www.nice.org.uk/guidance/ng59/resources/resource-impact-statement-2726444413 The number of finished consultant episodes (FCEs), by age and gender, was extracted to establish how many spinal fusion procedures were completed for people with a diagnosis of lower back pain. The data were used to confirm low levels of activity and consequently the associated cost impact would not be significant. Summary statistics: At a national level the data were used to estimate the number of people undergoing a spinal fusion with a diagnosis of lower back pain. The number of FCEs by age and gender were used by NICE’s resource impact assessment team to calculate the population likely to be affected by the guideline. Published: The resource impact statement has been published on the NICE website and does not contain any data. The statement is freely available (see link above). Benefits: NICE produces reports, templates and statements alongside our guidance. They detail the potential impact of guidance on finances and other resources (workforce, capacity and demand, infrastructure and training and education. Health and care organisations (NHS trusts and commissioners) will be aware that implementation of this guidance has the potential to generate costs savings, although they are not expected to be significant. 2. Use of data to support the development of a quality standard statement (NICE quality standard [QS152]) Output: Minutes October 2016, advisory committee decisions: https://www.nice.org.uk/guidance/qs152/documents/minutes Aggregated data taken from the resource impact assessment tool for NG50, originally extracted from HDIS, were used to estimate the number of people who had an unplanned admission related to cirrhosis/fibrosis of the liver, excluding alcoholic fibrosis or cirrhosis, and who underwent a procedure (OPCS codes extracted). Summary statistics: At a national level the data were used to estimate the number of admissions for cirrhosis/fibrosis and of those the number expected to undergo a procedure. The data were used to confirm a low level of activity relative to the potential number of people who might be screened in primary care for liver disease. Published: A high level summary of the committee discussion was published. The minutes of the meeting do not reference the use of HES data but are freely available. Benefits: NICE quality standards set out the priority areas for quality improvement in health and social care. They cover areas where there is variation in care. Each standard gives a set of statements to help improve quality and information on how to measure progress. NICE quality standards are used by primary and secondary care providers, local authorities and social care providers. The identification of liver disease in primary care was not selected as priority area for quality improvement. Specifically, for adults, the committee felt that there was a lack of specific guideline recommendations to support a quality statement and it was therefore reluctantly agreed that there could not be a quality statement about identifying NAFLD in higher-risk groups. The data helped to explain the resource impact of testing for liver disease in primary care relative to those who would require an intervention.
NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Institute for Health and Care Excellence (NICE) is the independent organisation responsible for providing evidence-based guidance on health and social care. NICE guidance, standards and other resources help health, public health and social care professionals deliver the best possible care within the resources available. Hospital Episode Statistics (HES) are used for the following: - during the scoping of guidance and standards; - at review; and - to develop resource impact assessment tools for commissioners/and providers. The data is used to evaluate activity both prior to and following publication of guidance/standards e.g. when developing guidance or deciding if it should be reviewed. This data allows NICE to establish current activity levels in the NHS and enables the resource impact of implementing guidance to be established with increased precision. NICE uses HES data during the development of general practice and CCG level indicators. This is work jointly completed by NICE and NHS Digital on behalf of NHS England. These indicators provide clear, comparative information about the quality of health services and may be included in national frameworks such as the QOF, CCGOIS and CCGIAF. Extract data at a national level but may need local level data for highly specialised technologies (small numbers of people) or for work associated with the cancer drugs fund. Data will only be used for purposes relating to the provision of healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	The data will be extracted from HDIS in an aggregated form and shared with guidance, guideline, indicator and tool developing teams within NICE. The data is shared using a secure distribution method (NICE Share is a HTTPS file sharing application based on Microsoft IIS. All communication is encrypted using HTTPS and authentication is required for any file access). The data will be used to: assess current activity levels in the NHS, in cost effectiveness analysis (economic modelling) and to cost the implementation (resource impact assessment) of NICE guidance. All data extracted from HDIS which includes small number are stored in network folders which have restricted access. The HDIS database and any extracted aggregate data which includes small numbers will only be access using the NICE network through desktop PCs. Any data held temporarily on portable equipment must be supported by a business case and must be encrypted to a minimum of AES25. To complete trend analysis NICE require access to the last five years of data. Analysis of the last full five years data enables patterns and trends to be established and allows NICE to assess the level of need/cost with increased precision. The data extracted from HDIS is compared to other datasets at a national level (England) e.g. prescribing data in the Innovation Scorecard. No data will be linked to record level patient data. The majority of the data extracted from HDIS is at a national level (England) however access to local level data (CCG or trust) is sometimes needed when developing resource impact assessment tools or during the scoping of guidance to establish if there is variation in practice across the country (England). Access to pseudonymised record level data is essential when considering guidance for rare conditions/diseases, as they often affect very small numbers of people. NICE has offices in Manchester and London, and the data extracted from HDIS will be stored and accessed using servers based onsite in these locations; each server is a mirror of the other. The security arrangements and policies are the same for both locations. HDIS is only accessed by Manchester based staff. Record level data will not be extracted from the database. All extracts taken from the database will be aggregate level which may contain small numbers. Data in format suitable for publication, after the small numbers policy has been applied (in line with the HES analysis guide) are shared with committee members. All committee members have signed confidentiality agreements. Outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide.	The data is being used for the development of guidance/standards that: are independent and authoritative, are based on the best available evidence and set out the best ways to prevent, diagnose and treat disease and ill health, promote healthy living, and care for vulnerable people. The National Institute for Health and Care Excellence (NICE) provides national guidance and advice to improve health and social care. HES data are used during guidance development and to produce tools to support its implementation. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any outputs that are produced from the system that are to be published or shared will be small number suppressed outputs in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. The results are provided to teams within NICE responsible for guidance/standard/indicator development. They are also discussed by guidance/indicator developing committees. Key documents used during the development of guidance/standards are published on NICE’s website. Only data in a format suitable for publication will be presented. These documents are available free of charge to the public. Outputs will contain only aggregate level of data and small numbers suppressed in line with the HES analysis guide/ small numbers policy. NICE publishes new guidance throughout the year and so this work is on-going. HES data is also used by NICE’s indicator development team during the development phase of QOF and CCG OIS indicators. This is work jointly undertaken by NICE and the NHS Digital, and is funded by NHS England. The data are discussed at indicator development committees but only once the small numbers policy has been applied (see above).	Evidence based guidance has clear benefits for the Health and Social Care sectors and for patients. The benefits of using the data extend beyond the term of the proposed contract. The uptake and impact of NICE guidance can be found here: https://www.nice.org.uk/About/What-we-do/Into-practice/Measuring-the-uptake-of-NICE-guidance. Measuring the impact of NICE guidance is complicated as many other factors also contribute to patient outcomes. Examples of benefits achieved using HDIS NICE uses HDIS HES data to support the development and implementation of NICE Guidance and Quality Standards in the following ways:- HES data are used to provide the NHS with information to help them understand the resource impact of implementing NICE guidance. NICE also uses the data to develop evidence based guidance. The data are used to understand the population likely to benefit from the introduction of new medicines and technologies. The following are two specific examples to illustrate how NICE uses this data: 1. Use of data to support implementation tools (NICE guidelines [NG59]) Output: Resource impact statement published November 2016 https://www.nice.org.uk/guidance/ng59/resources/resource-impact-statement-2726444413 The number of finished consultant episodes (FCEs), by age and gender, was extracted to establish how many spinal fusion procedures were completed for people with a diagnosis of lower back pain. The data were used to confirm low levels of activity and consequently the associated cost impact would not be significant. Summary statistics: At a national level the data were used to estimate the number of people undergoing a spinal fusion with a diagnosis of lower back pain. The number of FCEs by age and gender were used by NICE’s resource impact assessment team to calculate the population likely to be affected by the guideline. Published: The resource impact statement has been published on the NICE website and does not contain any data. The statement is freely available (see link above). Benefits: NICE produces reports, templates and statements alongside our guidance. They detail the potential impact of guidance on finances and other resources (workforce, capacity and demand, infrastructure and training and education. Health and care organisations (NHS trusts and commissioners) will be aware that implementation of this guidance has the potential to generate costs savings, although they are not expected to be significant. 2. Use of data to support the development of a quality standard statement (NICE quality standard [QS152]) Output: Minutes October 2016, advisory committee decisions: https://www.nice.org.uk/guidance/qs152/documents/minutes Aggregated data taken from the resource impact assessment tool for NG50, originally extracted from HDIS, were used to estimate the number of people who had an unplanned admission related to cirrhosis/fibrosis of the liver, excluding alcoholic fibrosis or cirrhosis, and who underwent a procedure (OPCS codes extracted). Summary statistics: At a national level the data were used to estimate the number of admissions for cirrhosis/fibrosis and of those the number expected to undergo a procedure. The data were used to confirm a low level of activity relative to the potential number of people who might be screened in primary care for liver disease. Published: A high level summary of the committee discussion was published. The minutes of the meeting do not reference the use of HES data but are freely available. Benefits: NICE quality standards set out the priority areas for quality improvement in health and social care. They cover areas where there is variation in care. Each standard gives a set of statements to help improve quality and information on how to measure progress. NICE quality standards are used by primary and secondary care providers, local authorities and social care providers. The identification of liver disease in primary care was not selected as priority area for quality improvement. Specifically, for adults, the committee felt that there was a lack of specific guideline recommendations to support a quality statement and it was therefore reluctantly agreed that there could not be a quality statement about identifying NAFLD in higher-risk groups. The data helped to explain the resource impact of testing for liver disease in primary care relative to those who would require an intervention.
NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE)	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Institute for Health and Care Excellence (NICE) is the independent organisation responsible for providing evidence-based guidance on health and social care. NICE guidance, standards and other resources help health, public health and social care professionals deliver the best possible care within the resources available. Hospital Episode Statistics (HES) are used for the following: - during the scoping of guidance and standards; - at review; and - to develop resource impact assessment tools for commissioners/and providers. The data is used to evaluate activity both prior to and following publication of guidance/standards e.g. when developing guidance or deciding if it should be reviewed. This data allows NICE to establish current activity levels in the NHS and enables the resource impact of implementing guidance to be established with increased precision. NICE uses HES data during the development of general practice and CCG level indicators. This is work jointly completed by NICE and NHS Digital on behalf of NHS England. These indicators provide clear, comparative information about the quality of health services and may be included in national frameworks such as the QOF, CCGOIS and CCGIAF. Extract data at a national level but may need local level data for highly specialised technologies (small numbers of people) or for work associated with the cancer drugs fund. Data will only be used for purposes relating to the provision of healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.	The data will be extracted from HDIS in an aggregated form and shared with guidance, guideline, indicator and tool developing teams within NICE. The data is shared using a secure distribution method (NICE Share is a HTTPS file sharing application based on Microsoft IIS. All communication is encrypted using HTTPS and authentication is required for any file access). The data will be used to: assess current activity levels in the NHS, in cost effectiveness analysis (economic modelling) and to cost the implementation (resource impact assessment) of NICE guidance. All data extracted from HDIS which includes small number are stored in network folders which have restricted access. The HDIS database and any extracted aggregate data which includes small numbers will only be access using the NICE network through desktop PCs. Any data held temporarily on portable equipment must be supported by a business case and must be encrypted to a minimum of AES25. To complete trend analysis NICE require access to the last five years of data. Analysis of the last full five years data enables patterns and trends to be established and allows NICE to assess the level of need/cost with increased precision. The data extracted from HDIS is compared to other datasets at a national level (England) e.g. prescribing data in the Innovation Scorecard. No data will be linked to record level patient data. The majority of the data extracted from HDIS is at a national level (England) however access to local level data (CCG or trust) is sometimes needed when developing resource impact assessment tools or during the scoping of guidance to establish if there is variation in practice across the country (England). Access to pseudonymised record level data is essential when considering guidance for rare conditions/diseases, as they often affect very small numbers of people. NICE has offices in Manchester and London, and the data extracted from HDIS will be stored and accessed using servers based onsite in these locations; each server is a mirror of the other. The security arrangements and policies are the same for both locations. HDIS is only accessed by Manchester based staff. Record level data will not be extracted from the database. All extracts taken from the database will be aggregate level which may contain small numbers. Data in format suitable for publication, after the small numbers policy has been applied (in line with the HES analysis guide) are shared with committee members. All committee members have signed confidentiality agreements. Outputs will contain only aggregate level data with small numbers suppressed in line with the HES analysis guide.	The data is being used for the development of guidance/standards that: are independent and authoritative, are based on the best available evidence and set out the best ways to prevent, diagnose and treat disease and ill health, promote healthy living, and care for vulnerable people. The National Institute for Health and Care Excellence (NICE) provides national guidance and advice to improve health and social care. HES data are used during guidance development and to produce tools to support its implementation. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any outputs that are produced from the system that are to be published or shared will be small number suppressed outputs in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. The results are provided to teams within NICE responsible for guidance/standard/indicator development. They are also discussed by guidance/indicator developing committees. Key documents used during the development of guidance/standards are published on NICE’s website. Only data in a format suitable for publication will be presented. These documents are available free of charge to the public. Outputs will contain only aggregate level of data and small numbers suppressed in line with the HES analysis guide/ small numbers policy. NICE publishes new guidance throughout the year and so this work is on-going. HES data is also used by NICE’s indicator development team during the development phase of QOF and CCG OIS indicators. This is work jointly undertaken by NICE and the NHS Digital, and is funded by NHS England. The data are discussed at indicator development committees but only once the small numbers policy has been applied (see above).	Evidence based guidance has clear benefits for the Health and Social Care sectors and for patients. The benefits of using the data extend beyond the term of the proposed contract. The uptake and impact of NICE guidance can be found here: https://www.nice.org.uk/About/What-we-do/Into-practice/Measuring-the-uptake-of-NICE-guidance. Measuring the impact of NICE guidance is complicated as many other factors also contribute to patient outcomes. Examples of benefits achieved using HDIS NICE uses HDIS HES data to support the development and implementation of NICE Guidance and Quality Standards in the following ways:- HES data are used to provide the NHS with information to help them understand the resource impact of implementing NICE guidance. NICE also uses the data to develop evidence based guidance. The data are used to understand the population likely to benefit from the introduction of new medicines and technologies. The following are two specific examples to illustrate how NICE uses this data: 1. Use of data to support implementation tools (NICE guidelines [NG59]) Output: Resource impact statement published November 2016 https://www.nice.org.uk/guidance/ng59/resources/resource-impact-statement-2726444413 The number of finished consultant episodes (FCEs), by age and gender, was extracted to establish how many spinal fusion procedures were completed for people with a diagnosis of lower back pain. The data were used to confirm low levels of activity and consequently the associated cost impact would not be significant. Summary statistics: At a national level the data were used to estimate the number of people undergoing a spinal fusion with a diagnosis of lower back pain. The number of FCEs by age and gender were used by NICE’s resource impact assessment team to calculate the population likely to be affected by the guideline. Published: The resource impact statement has been published on the NICE website and does not contain any data. The statement is freely available (see link above). Benefits: NICE produces reports, templates and statements alongside our guidance. They detail the potential impact of guidance on finances and other resources (workforce, capacity and demand, infrastructure and training and education. Health and care organisations (NHS trusts and commissioners) will be aware that implementation of this guidance has the potential to generate costs savings, although they are not expected to be significant. 2. Use of data to support the development of a quality standard statement (NICE quality standard [QS152]) Output: Minutes October 2016, advisory committee decisions: https://www.nice.org.uk/guidance/qs152/documents/minutes Aggregated data taken from the resource impact assessment tool for NG50, originally extracted from HDIS, were used to estimate the number of people who had an unplanned admission related to cirrhosis/fibrosis of the liver, excluding alcoholic fibrosis or cirrhosis, and who underwent a procedure (OPCS codes extracted). Summary statistics: At a national level the data were used to estimate the number of admissions for cirrhosis/fibrosis and of those the number expected to undergo a procedure. The data were used to confirm a low level of activity relative to the potential number of people who might be screened in primary care for liver disease. Published: A high level summary of the committee discussion was published. The minutes of the meeting do not reference the use of HES data but are freely available. Benefits: NICE quality standards set out the priority areas for quality improvement in health and social care. They cover areas where there is variation in care. Each standard gives a set of statements to help improve quality and information on how to measure progress. NICE quality standards are used by primary and secondary care providers, local authorities and social care providers. The identification of liver disease in primary care was not selected as priority area for quality improvement. Specifically, for adults, the committee felt that there was a lack of specific guideline recommendations to support a quality statement and it was therefore reluctantly agreed that there could not be a quality statement about identifying NAFLD in higher-risk groups. The data helped to explain the resource impact of testing for liver disease in primary care relative to those who would require an intervention.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use CYPHS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHLDDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use DIDs linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use IAPT linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHMDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHSDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MSDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number to undertake invoice validation on behalf of the CCG. In order to support commissioning of patient care by validating non-contracted activity in the CCG, this data is required for the purpose of invoice validation. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF.	1) Central and Midlands DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the Midlands and Lancashire CSU (Data Processor 1). 2) The CSU carry out the following processing activities within the CEfF for invoice validation purposes: a) Checking the individual is registered to a particular Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow b) Once the backing information is received, this will be checked against system access and reports provided by the NHS Digital to confirm the payments are: - In line with Payment by Results tariffs - are in relation to a patient registered with a CCG GP or resident within the CCG area. - The health care provided should be paid by the CCG in line with CCG guidance.  3) The CCG are notified of that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between the CSU and the provider meaning that no data needs to be sent to the CCG. The CCG only receives notification to pay.	1) Addressing poor data quality issues 2) Production of reports for business intelligence 3) Budget reporting 4) Validation of invoices for non-contracted events	1) Financial validation of activity 2) CCG Budget control 3) Commissioning and performance management 4) Meeting commissioning objectives without compromising patient confidentiality 5) The avoidance of misapproproation of public funds to ensure the on-going delivery of patient care
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Local Provider Data- Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. Central and Midlands DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2) Data quality management of data is completed by the DSCRO and the identifiable data is then passed securely to North England CSU for the addition of derived fields, linkage of data sets and analysis. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) Patient level data will not be shared outside of the CCG. External aggregated reports only.	1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level. 8) GP Practice level dashboard reports include high flyers.	1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Health economic modelling using: a) Analysis on provider performance against 18 weeks wait targets b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway d) Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows 3) Commissioning cycle support for grouping and re-costing previous activity 4) Enables monitoring of: a) CCG outcome indicators b) Non-financial validation of patient level data c) Successful delivery of integrated care within the CCG d) Checking frequent or multiple attendances to improve early intervention and avoid admissions e) Commissioning and performance management 5) Feedback to NHS service providers on data quality at an aggregate level
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	SUS- Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. Central and Midlands DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2) Data quality management of data is completed by the DSCRO and the identifiable data is then passed securely to North England CSU for the addition of derived fields, linkage of data sets and analysis. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) Patient level data will not be shared outside of the CCG. External aggregated reports only.	1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level. 8) GP Practice level dashboard reports include high flyers.	1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Health economic modelling using: a) Analysis on provider performance against 18 weeks wait targets b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway d) Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows 3) Commissioning cycle support for grouping and re-costing previous activity 4) Enables monitoring of: a) CCG outcome indicators b) Non-financial validation of patient level data c) Successful delivery of integrated care within the CCG d) Checking frequent or multiple attendances to improve early intervention and avoid admissions e) Commissioning and performance management 5) Feedback to NHS service providers on data quality at an aggregate level
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Acute	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.	1) SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from Central and Midlands Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2) Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Midlands and Lancashire CSU (Data Processor 1), who hold the SUS data within the secure Data Centre on N3. 3) SUS data is linked to GP data in the risk stratification tool by the data processor. 4) As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 5) Midlands and Lancashire CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 6) Once Midlands and Lancashire CSU has completed the processing, the CCG can dial in to the online system via N3 connection to access the data anonymised at patient level.	1) As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 2) Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk with no identifiers 3) Record level output will be available for commissioners in anonymised or pseudonymised format. 4) GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.	Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1) Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2) Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 3) Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 4) Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 5) Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. All of the above lead to improved patient experience through more effective commissioning of services.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use CYPHS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHLDDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use DIDs linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use IAPT linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHMDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MHSDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Application for the CCG to use MSDS linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level. 6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with NHS Digital guidance.	Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS 1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level.	Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3) Health economic modelling using: (a) Analysis on provider performance. (b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. (c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway. 4) Commissioning cycle support for grouping and re-costing previous activity. 5) Enables monitoring of: (a) CCG outcome indicators. (b) Non-financial validation of activity. (c) Successful delivery of integrated care within the CCG. (d) Checking frequent or multiple attendances to improve early intervention and avoid admissions. (e) Case management. (f) Care service planning. (g) Commissioning and performance management. (h) List size verification by GP practices. (i) Understanding the care of patients in nursing homes. 6) Feedback to NHS service providers on data quality at an aggregate and individual record level.
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number to undertake invoice validation on behalf of the CCG. In order to support commissioning of patient care by validating non-contracted activity in the CCG, this data is required for the purpose of invoice validation. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF.	1) Central and Midlands DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the Midlands and Lancashire CSU (Data Processor 1). 2) The CSU carry out the following processing activities within the CEfF for invoice validation purposes: a) Checking the individual is registered to a particular Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow b) Once the backing information is received, this will be checked against system access and reports provided by the NHS Digital to confirm the payments are: - In line with Payment by Results tariffs - are in relation to a patient registered with a CCG GP or resident within the CCG area. - The health care provided should be paid by the CCG in line with CCG guidance.  3) The CCG are notified of that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between the CSU and the provider meaning that no data needs to be sent to the CCG. The CCG only receives notification to pay.	1) Addressing poor data quality issues 2) Production of reports for business intelligence 3) Budget reporting 4) Validation of invoices for non-contracted events	1) Financial validation of activity 2) CCG Budget control 3) Commissioning and performance management 4) Meeting commissioning objectives without compromising patient confidentiality 5) The avoidance of misapproproation of public funds to ensure the on-going delivery of patient care
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Local Provider Data- Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. Central and Midlands DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2) Data quality management of data is completed by the DSCRO and the identifiable data is then passed securely to North England CSU for the addition of derived fields, linkage of data sets and analysis. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) Patient level data will not be shared outside of the CCG. External aggregated reports only.	1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level. 8) GP Practice level dashboard reports include high flyers.	1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Health economic modelling using: a) Analysis on provider performance against 18 weeks wait targets b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway d) Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows 3) Commissioning cycle support for grouping and re-costing previous activity 4) Enables monitoring of: a) CCG outcome indicators b) Non-financial validation of patient level data c) Successful delivery of integrated care within the CCG d) Checking frequent or multiple attendances to improve early intervention and avoid admissions e) Commissioning and performance management 5) Feedback to NHS service providers on data quality at an aggregate level
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	SUS- Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.	1) Central and Midlands Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. Central and Midlands DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2) Data quality management of data is completed by the DSCRO and the identifiable data is then passed securely to North England CSU for the addition of derived fields, linkage of data sets and analysis. 3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) Patient level data will not be shared outside of the CCG. External aggregated reports only.	1) Commissioner reporting: (a) Summary by provider view - plan & actuals year to date (YTD). (b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD. (c) Summary by provider view - activity & finance variance by POD. (d) Planned care by provider view - activity & finance plan & actuals YTD. (e) Planned care by POD view - activity plan & actuals YTD. (f) Provider reporting. (g) Statutory returns. (h) Statutory returns - monthly activity return. (i) Statutory returns - quarterly activity return. (j) Delayed discharges. (k) Quality & performance referral to treatment reporting. 2) Readmissions analysis. 3) Production of aggregate reports for CCG Business Intelligence. 4) Production of project / programme level dashboards. 5) Monitoring of acute / community / mental health quality matrix. 6) Clinical coding reviews / audits. 7) Budget reporting down to individual GP Practice level. 8) GP Practice level dashboard reports include high flyers.	1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways. 2) Health economic modelling using: a) Analysis on provider performance against 18 weeks wait targets b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway d) Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows 3) Commissioning cycle support for grouping and re-costing previous activity 4) Enables monitoring of: a) CCG outcome indicators b) Non-financial validation of patient level data c) Successful delivery of integrated care within the CCG d) Checking frequent or multiple attendances to improve early intervention and avoid admissions e) Commissioning and performance management 5) Feedback to NHS service providers on data quality at an aggregate level
NHS BLACKBURN WITH DARWEN CCG	NHS BLACKBURN WITH DARWEN CCG	Local Provider Data - Demand For service	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.	1) SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from Central and Midlands Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2) Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Midlands and Lancashire CSU (Data Processor 1), who hold the SUS data within the secure Data Centre on N3. 3) SUS data is linked to GP data in the risk stratification tool by the data processor. 4) As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 5) Midlands and Lancashire CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 6) Once Midlands and Lancashire CSU has completed the processing, the CCG can dial in to the online system via N3 connection to access the data anonymised at patient level.	1) As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 2) Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk with no identifiers 3) Record level output will be available for commissioners in anonymised or pseudonymised format. 4) GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.	Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1) Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2) Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 3) Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 4) Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 5) Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. All of the above lead to improved patient experience through more effective commissioning of services.
NHS BLOOD AND TRANSPLANT (NHSBT)	NHS BLOOD AND TRANSPLANT (NHSBT)	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to NHS Blood and Transplant will be used only for the approved Medical research project
NHS BLOOD AND TRANSPLANT (NHSBT)	NHS BLOOD AND TRANSPLANT (NHSBT)	MRIS - Cause of Death Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to NHS Blood and Transplant will be used only for the approved Medical research project
NHS BLOOD AND TRANSPLANT (NHSBT)	NHS BLOOD AND TRANSPLANT (NHSBT)	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to NHS Blood and Transplant will be used only for the approved Medical research project
NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations. The CSU needs: • The provision of analytically based intelligence for a range of Clinical Commissioning Groups (CCGs) for benchmarking of similar health economies or populations in England, not just in the CSU’s area. • To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways. • To support large scale transformation projects that may impact several commissioners (CCGs) • Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive. • Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes • Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use • Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes • To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy The specific services and products that will utilise the data are the following :- A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established. C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand. D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF). The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates. E. Other specific projects are: 1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally. 2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models. 3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.	The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. SUS PBR As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs. PROMS As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores. The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes. Mental Health Minimum Dataset (MHMDS) The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data. As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required. Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports. The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. ONS mortality data As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment. Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access. The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain. For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor.	Previous outputs: A. QIPP opportunity packs – these reports provide in-depth information to support commissioning organisations in developing their strategic plans. The focus of these reports is comparative information on utilisation rates for subsets of acute hospital activity (inpatient, outpatient, and A&E) that are amenable to interventions targeted at reducing levels of acute hospital activity. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. The CSU have produced similar reports for several years. In a typical year the CSU might expect to produce about 30 such reports. B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. C. Mental Health activity modelling – in 2017, the CSU undertook a substantial project looking at the physical health of people who use mental health services. The CSU produced a series of analyses that highlighted significantly poorer health outcomes for mental health patients. The CSU produced locally-focussed reports for a number of commissioning organisations, before NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. D. Impact of demography – for several years the CSU have produced reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The focus of these reports is the effect of changes in population size, age structure and health status on levels of acute healthcare activity across a range of delivery points. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. In a typical year the CSU might expect to produce about 30 such reports. In 2017, the CSU produced a report for NHS England describing the context and status of end of life care services across the West Midlands Region. Sustainability and Transformation Partnerships need to include proposals to improve choice in end of life care in their strategic plans. A second report focussed on palliative and end of life care for children and young people was later commissioned by NHS England to help understand characteristics and levels of resource required by children with life-limiting and or life-threatening conditions. All our reports/outputs conform to relevant legislation and guidance with respect to confidentiality and other important considerations. Planned outputs: A. QIPP opportunity packs – as in previous years, the CSU has been tasked with producing reports that provide in-depth information to support commissioning organisations in developing their strategic plans. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. In particular, the CSU have been asked to consider the relationship between surgeon specialisation and patient outcomes. Most studies looking at the relationship between surgical activity and outcomes have focussed on procedure volume i.e. the volume-outcome relationship. But recently, the existence of a specialisation-outcome that is independent of the volume-outcome relationship has been advanced. C. Mental Health activity modelling – the CSU expect to produce a number of follow-up analyses based on previous work looking at the physical health of people who use mental health services. The exact focus of this work is yet to be confirmed but may include in-depth reviews of specific patient groups e.g. CAMHS, substance misuse; pathway modelling; or exploring relationships with other datasets e.g. primary care, IAPT. D. Impact of demography – as in previous years, the CSU has been tasked with producing reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). In 2017-18 the CSU has been tasked with further developing our methods for understanding the impact of demographic changes on future healthcare utilisation. The CSU intend to do this by drawing on the relationship between healthcare use and proximity to death. The proposed methods will require combining mortality data and hospital activity data.	As described by the examples listed above, the CSU’s work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work is limited to the health and social care arena and outputs will be used only by health and social care organisations. With respect to the outputs listed above, the CSU wishes to highlight the following benefits: A. QIPP opportunity packs – these reports continue to help focus commissioner plans and direct resources to areas most likely to lead to improvements in quality, outcomes, and cost savings. B. PROMS decision support tool – the CSU’s work on a decision support tool is aimed at helping patients and clinicians make improved joint decisions about whether to undergo joint replacement surgery. This would help to minimise both the financial cost of such procedures and avoid unnecessary pain and risk for some patients who are unlikely to experience benefits. C. Mental Health activity modelling – in 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. The objectives of the report were to highlight the level of health inequalities experienced by users of specialist mental health services, to provide insight on the use of acute hospital services by mental health service users, identify groups that could benefit from targeted interventions, and provide a summary of effective interventions for improving the physical health of mental health service users. An integrated mental and physical health approach is one of the three priority actions described in the Five Year Forward View for Mental Health. In 2017-18, the CSU are committed to further work focussed on some of the issues uncovered by the report. D. Impact of demography – understanding how demographic change will impact on population healthcare use is a central question for healthcare planners. It sets the scale of the financial challenge in health economy plans and underpins all large scale healthcare reconfigurations and long-term healthcare contracts. Overstating or understating the impact of demographic pressures may lead a health economy to set unduly radical or conservative plans for cost savings, by helping health economies to produce improved estimates of the likely impact this risk is mitigated. Palliative and end of life care – improving palliative and end of life care is a Department of Health commitment https://www.gov.uk/government/publications/choice-in-end-of-life-care-government-response The CSU’s reports in this area highlight variation and provide greater transparency around current practice. The CSU are not the end user of the outputs they produce, however they regularly receive positive feedback from their customers and currently receive repeat business from around 75% of customers. Note on CSU's customer base: With the introduction of STPs, the CSU's customer base has rapidly become a collective local 'health and care economy', comprising a number of different organisation types within the NHS. For this reason, all parties involved in STPs are referred to as customers. Depending on how an individual STP chooses to operates, different members may be directed to take responsibly for particular programmes of work such that the CSU could be directly commissioned by any member organisation on behalf of the collective STP. In 2016, Public Health England appointed the CSU to a 4-year framework agreement to supply data science and health impact assessment services. In 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 Sustainability and Transformation Partnerships. The end users of these reports are all the organisations that make up local STPs.
NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations. The CSU needs: • The provision of analytically based intelligence for a range of Clinical Commissioning Groups (CCGs) for benchmarking of similar health economies or populations in England, not just in the CSU’s area. • To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways. • To support large scale transformation projects that may impact several commissioners (CCGs) • Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive. • Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes • Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use • Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes • To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy The specific services and products that will utilise the data are the following :- A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established. C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand. D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF). The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates. E. Other specific projects are: 1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally. 2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models. 3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.	The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. SUS PBR As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs. PROMS As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores. The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes. Mental Health Minimum Dataset (MHMDS) The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data. As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required. Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports. The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. ONS mortality data As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment. Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access. The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain. For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor.	Previous outputs: A. QIPP opportunity packs – these reports provide in-depth information to support commissioning organisations in developing their strategic plans. The focus of these reports is comparative information on utilisation rates for subsets of acute hospital activity (inpatient, outpatient, and A&E) that are amenable to interventions targeted at reducing levels of acute hospital activity. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. The CSU have produced similar reports for several years. In a typical year the CSU might expect to produce about 30 such reports. B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. C. Mental Health activity modelling – in 2017, the CSU undertook a substantial project looking at the physical health of people who use mental health services. The CSU produced a series of analyses that highlighted significantly poorer health outcomes for mental health patients. The CSU produced locally-focussed reports for a number of commissioning organisations, before NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. D. Impact of demography – for several years the CSU have produced reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The focus of these reports is the effect of changes in population size, age structure and health status on levels of acute healthcare activity across a range of delivery points. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. In a typical year the CSU might expect to produce about 30 such reports. In 2017, the CSU produced a report for NHS England describing the context and status of end of life care services across the West Midlands Region. Sustainability and Transformation Partnerships need to include proposals to improve choice in end of life care in their strategic plans. A second report focussed on palliative and end of life care for children and young people was later commissioned by NHS England to help understand characteristics and levels of resource required by children with life-limiting and or life-threatening conditions. All our reports/outputs conform to relevant legislation and guidance with respect to confidentiality and other important considerations. Planned outputs: A. QIPP opportunity packs – as in previous years, the CSU has been tasked with producing reports that provide in-depth information to support commissioning organisations in developing their strategic plans. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. In particular, the CSU have been asked to consider the relationship between surgeon specialisation and patient outcomes. Most studies looking at the relationship between surgical activity and outcomes have focussed on procedure volume i.e. the volume-outcome relationship. But recently, the existence of a specialisation-outcome that is independent of the volume-outcome relationship has been advanced. C. Mental Health activity modelling – the CSU expect to produce a number of follow-up analyses based on previous work looking at the physical health of people who use mental health services. The exact focus of this work is yet to be confirmed but may include in-depth reviews of specific patient groups e.g. CAMHS, substance misuse; pathway modelling; or exploring relationships with other datasets e.g. primary care, IAPT. D. Impact of demography – as in previous years, the CSU has been tasked with producing reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). In 2017-18 the CSU has been tasked with further developing our methods for understanding the impact of demographic changes on future healthcare utilisation. The CSU intend to do this by drawing on the relationship between healthcare use and proximity to death. The proposed methods will require combining mortality data and hospital activity data.	As described by the examples listed above, the CSU’s work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work is limited to the health and social care arena and outputs will be used only by health and social care organisations. With respect to the outputs listed above, the CSU wishes to highlight the following benefits: A. QIPP opportunity packs – these reports continue to help focus commissioner plans and direct resources to areas most likely to lead to improvements in quality, outcomes, and cost savings. B. PROMS decision support tool – the CSU’s work on a decision support tool is aimed at helping patients and clinicians make improved joint decisions about whether to undergo joint replacement surgery. This would help to minimise both the financial cost of such procedures and avoid unnecessary pain and risk for some patients who are unlikely to experience benefits. C. Mental Health activity modelling – in 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. The objectives of the report were to highlight the level of health inequalities experienced by users of specialist mental health services, to provide insight on the use of acute hospital services by mental health service users, identify groups that could benefit from targeted interventions, and provide a summary of effective interventions for improving the physical health of mental health service users. An integrated mental and physical health approach is one of the three priority actions described in the Five Year Forward View for Mental Health. In 2017-18, the CSU are committed to further work focussed on some of the issues uncovered by the report. D. Impact of demography – understanding how demographic change will impact on population healthcare use is a central question for healthcare planners. It sets the scale of the financial challenge in health economy plans and underpins all large scale healthcare reconfigurations and long-term healthcare contracts. Overstating or understating the impact of demographic pressures may lead a health economy to set unduly radical or conservative plans for cost savings, by helping health economies to produce improved estimates of the likely impact this risk is mitigated. Palliative and end of life care – improving palliative and end of life care is a Department of Health commitment https://www.gov.uk/government/publications/choice-in-end-of-life-care-government-response The CSU’s reports in this area highlight variation and provide greater transparency around current practice. The CSU are not the end user of the outputs they produce, however they regularly receive positive feedback from their customers and currently receive repeat business from around 75% of customers. Note on CSU's customer base: With the introduction of STPs, the CSU's customer base has rapidly become a collective local 'health and care economy', comprising a number of different organisation types within the NHS. For this reason, all parties involved in STPs are referred to as customers. Depending on how an individual STP chooses to operates, different members may be directed to take responsibly for particular programmes of work such that the CSU could be directly commissioned by any member organisation on behalf of the collective STP. In 2016, Public Health England appointed the CSU to a 4-year framework agreement to supply data science and health impact assessment services. In 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 Sustainability and Transformation Partnerships. The end users of these reports are all the organisations that make up local STPs.
NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations. The CSU needs: • The provision of analytically based intelligence for a range of Clinical Commissioning Groups (CCGs) for benchmarking of similar health economies or populations in England, not just in the CSU’s area. • To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways. • To support large scale transformation projects that may impact several commissioners (CCGs) • Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive. • Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes • Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use • Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes • To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy The specific services and products that will utilise the data are the following :- A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established. C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand. D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF). The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates. E. Other specific projects are: 1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally. 2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models. 3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.	The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. SUS PBR As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs. PROMS As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores. The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes. Mental Health Minimum Dataset (MHMDS) The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data. As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required. Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports. The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. ONS mortality data As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment. Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access. The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain. For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor.	Previous outputs: A. QIPP opportunity packs – these reports provide in-depth information to support commissioning organisations in developing their strategic plans. The focus of these reports is comparative information on utilisation rates for subsets of acute hospital activity (inpatient, outpatient, and A&E) that are amenable to interventions targeted at reducing levels of acute hospital activity. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. The CSU have produced similar reports for several years. In a typical year the CSU might expect to produce about 30 such reports. B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. C. Mental Health activity modelling – in 2017, the CSU undertook a substantial project looking at the physical health of people who use mental health services. The CSU produced a series of analyses that highlighted significantly poorer health outcomes for mental health patients. The CSU produced locally-focussed reports for a number of commissioning organisations, before NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. D. Impact of demography – for several years the CSU have produced reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The focus of these reports is the effect of changes in population size, age structure and health status on levels of acute healthcare activity across a range of delivery points. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. In a typical year the CSU might expect to produce about 30 such reports. In 2017, the CSU produced a report for NHS England describing the context and status of end of life care services across the West Midlands Region. Sustainability and Transformation Partnerships need to include proposals to improve choice in end of life care in their strategic plans. A second report focussed on palliative and end of life care for children and young people was later commissioned by NHS England to help understand characteristics and levels of resource required by children with life-limiting and or life-threatening conditions. All our reports/outputs conform to relevant legislation and guidance with respect to confidentiality and other important considerations. Planned outputs: A. QIPP opportunity packs – as in previous years, the CSU has been tasked with producing reports that provide in-depth information to support commissioning organisations in developing their strategic plans. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. In particular, the CSU have been asked to consider the relationship between surgeon specialisation and patient outcomes. Most studies looking at the relationship between surgical activity and outcomes have focussed on procedure volume i.e. the volume-outcome relationship. But recently, the existence of a specialisation-outcome that is independent of the volume-outcome relationship has been advanced. C. Mental Health activity modelling – the CSU expect to produce a number of follow-up analyses based on previous work looking at the physical health of people who use mental health services. The exact focus of this work is yet to be confirmed but may include in-depth reviews of specific patient groups e.g. CAMHS, substance misuse; pathway modelling; or exploring relationships with other datasets e.g. primary care, IAPT. D. Impact of demography – as in previous years, the CSU has been tasked with producing reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). In 2017-18 the CSU has been tasked with further developing our methods for understanding the impact of demographic changes on future healthcare utilisation. The CSU intend to do this by drawing on the relationship between healthcare use and proximity to death. The proposed methods will require combining mortality data and hospital activity data.	As described by the examples listed above, the CSU’s work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work is limited to the health and social care arena and outputs will be used only by health and social care organisations. With respect to the outputs listed above, the CSU wishes to highlight the following benefits: A. QIPP opportunity packs – these reports continue to help focus commissioner plans and direct resources to areas most likely to lead to improvements in quality, outcomes, and cost savings. B. PROMS decision support tool – the CSU’s work on a decision support tool is aimed at helping patients and clinicians make improved joint decisions about whether to undergo joint replacement surgery. This would help to minimise both the financial cost of such procedures and avoid unnecessary pain and risk for some patients who are unlikely to experience benefits. C. Mental Health activity modelling – in 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. The objectives of the report were to highlight the level of health inequalities experienced by users of specialist mental health services, to provide insight on the use of acute hospital services by mental health service users, identify groups that could benefit from targeted interventions, and provide a summary of effective interventions for improving the physical health of mental health service users. An integrated mental and physical health approach is one of the three priority actions described in the Five Year Forward View for Mental Health. In 2017-18, the CSU are committed to further work focussed on some of the issues uncovered by the report. D. Impact of demography – understanding how demographic change will impact on population healthcare use is a central question for healthcare planners. It sets the scale of the financial challenge in health economy plans and underpins all large scale healthcare reconfigurations and long-term healthcare contracts. Overstating or understating the impact of demographic pressures may lead a health economy to set unduly radical or conservative plans for cost savings, by helping health economies to produce improved estimates of the likely impact this risk is mitigated. Palliative and end of life care – improving palliative and end of life care is a Department of Health commitment https://www.gov.uk/government/publications/choice-in-end-of-life-care-government-response The CSU’s reports in this area highlight variation and provide greater transparency around current practice. The CSU are not the end user of the outputs they produce, however they regularly receive positive feedback from their customers and currently receive repeat business from around 75% of customers. Note on CSU's customer base: With the introduction of STPs, the CSU's customer base has rapidly become a collective local 'health and care economy', comprising a number of different organisation types within the NHS. For this reason, all parties involved in STPs are referred to as customers. Depending on how an individual STP chooses to operates, different members may be directed to take responsibly for particular programmes of work such that the CSU could be directly commissioned by any member organisation on behalf of the collective STP. In 2016, Public Health England appointed the CSU to a 4-year framework agreement to supply data science and health impact assessment services. In 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 Sustainability and Transformation Partnerships. The end users of these reports are all the organisations that make up local STPs.
NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	NHS CENTRAL MIDLANDS COMMISSIONING SUPPORT UNIT	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations. The CSU needs: • The provision of analytically based intelligence for a range of Clinical Commissioning Groups (CCGs) for benchmarking of similar health economies or populations in England, not just in the CSU’s area. • To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways. • To support large scale transformation projects that may impact several commissioners (CCGs) • Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive. • Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes • Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use • Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes • To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy The specific services and products that will utilise the data are the following :- A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established. C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand. D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF). The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates. E. Other specific projects are: 1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally. 2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models. 3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.	The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. SUS PBR As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs. PROMS As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores. The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes. Mental Health Minimum Dataset (MHMDS) The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data. As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required. Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports. The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. ONS mortality data As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment. Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access. The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain. For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor.	Previous outputs: A. QIPP opportunity packs – these reports provide in-depth information to support commissioning organisations in developing their strategic plans. The focus of these reports is comparative information on utilisation rates for subsets of acute hospital activity (inpatient, outpatient, and A&E) that are amenable to interventions targeted at reducing levels of acute hospital activity. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. The CSU have produced similar reports for several years. In a typical year the CSU might expect to produce about 30 such reports. B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. C. Mental Health activity modelling – in 2017, the CSU undertook a substantial project looking at the physical health of people who use mental health services. The CSU produced a series of analyses that highlighted significantly poorer health outcomes for mental health patients. The CSU produced locally-focussed reports for a number of commissioning organisations, before NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. D. Impact of demography – for several years the CSU have produced reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The focus of these reports is the effect of changes in population size, age structure and health status on levels of acute healthcare activity across a range of delivery points. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. In a typical year the CSU might expect to produce about 30 such reports. In 2017, the CSU produced a report for NHS England describing the context and status of end of life care services across the West Midlands Region. Sustainability and Transformation Partnerships need to include proposals to improve choice in end of life care in their strategic plans. A second report focussed on palliative and end of life care for children and young people was later commissioned by NHS England to help understand characteristics and levels of resource required by children with life-limiting and or life-threatening conditions. All our reports/outputs conform to relevant legislation and guidance with respect to confidentiality and other important considerations. Planned outputs: A. QIPP opportunity packs – as in previous years, the CSU has been tasked with producing reports that provide in-depth information to support commissioning organisations in developing their strategic plans. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. In particular, the CSU have been asked to consider the relationship between surgeon specialisation and patient outcomes. Most studies looking at the relationship between surgical activity and outcomes have focussed on procedure volume i.e. the volume-outcome relationship. But recently, the existence of a specialisation-outcome that is independent of the volume-outcome relationship has been advanced. C. Mental Health activity modelling – the CSU expect to produce a number of follow-up analyses based on previous work looking at the physical health of people who use mental health services. The exact focus of this work is yet to be confirmed but may include in-depth reviews of specific patient groups e.g. CAMHS, substance misuse; pathway modelling; or exploring relationships with other datasets e.g. primary care, IAPT. D. Impact of demography – as in previous years, the CSU has been tasked with producing reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs). In 2017-18 the CSU has been tasked with further developing our methods for understanding the impact of demographic changes on future healthcare utilisation. The CSU intend to do this by drawing on the relationship between healthcare use and proximity to death. The proposed methods will require combining mortality data and hospital activity data.	As described by the examples listed above, the CSU’s work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work is limited to the health and social care arena and outputs will be used only by health and social care organisations. With respect to the outputs listed above, the CSU wishes to highlight the following benefits: A. QIPP opportunity packs – these reports continue to help focus commissioner plans and direct resources to areas most likely to lead to improvements in quality, outcomes, and cost savings. B. PROMS decision support tool – the CSU’s work on a decision support tool is aimed at helping patients and clinicians make improved joint decisions about whether to undergo joint replacement surgery. This would help to minimise both the financial cost of such procedures and avoid unnecessary pain and risk for some patients who are unlikely to experience benefits. C. Mental Health activity modelling – in 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. The objectives of the report were to highlight the level of health inequalities experienced by users of specialist mental health services, to provide insight on the use of acute hospital services by mental health service users, identify groups that could benefit from targeted interventions, and provide a summary of effective interventions for improving the physical health of mental health service users. An integrated mental and physical health approach is one of the three priority actions described in the Five Year Forward View for Mental Health. In 2017-18, the CSU are committed to further work focussed on some of the issues uncovered by the report. D. Impact of demography – understanding how demographic change will impact on population healthcare use is a central question for healthcare planners. It sets the scale of the financial challenge in health economy plans and underpins all large scale healthcare reconfigurations and long-term healthcare contracts. Overstating or understating the impact of demographic pressures may lead a health economy to set unduly radical or conservative plans for cost savings, by helping health economies to produce improved estimates of the likely impact this risk is mitigated. Palliative and end of life care – improving palliative and end of life care is a Department of Health commitment https://www.gov.uk/government/publications/choice-in-end-of-life-care-government-response The CSU’s reports in this area highlight variation and provide greater transparency around current practice. The CSU are not the end user of the outputs they produce, however they regularly receive positive feedback from their customers and currently receive repeat business from around 75% of customers. Note on CSU's customer base: With the introduction of STPs, the CSU's customer base has rapidly become a collective local 'health and care economy', comprising a number of different organisation types within the NHS. For this reason, all parties involved in STPs are referred to as customers. Depending on how an individual STP chooses to operates, different members may be directed to take responsibly for particular programmes of work such that the CSU could be directly commissioned by any member organisation on behalf of the collective STP. In 2016, Public Health England appointed the CSU to a 4-year framework agreement to supply data science and health impact assessment services. In 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 Sustainability and Transformation Partnerships. The end users of these reports are all the organisations that make up local STPs.
NHS ENGLAND	NHS ENGLAND	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS England supports across a wide spectrum of responsibilities to support Health and Social care within England access to HDIS is required to support this for the following areas; - commissioning - policy - finance - economic development - research and analysis The above all assist NHS England in its aim to create the culture and conditions for health and care services and staff to deliver the highest standard of care and ensure that valuable public resources are used effectively to get the best outcomes for individuals, communities and society for now and for future generations. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014. HDIS users are based across the organisation and their access is from the secure NHS England environment. They help NHS England to oversee the delivery of NHS funded services and the continuous improvements to the quality of treatment and care by using HES data to inform, target, strategies, monitor, benchmark, cross-check and plan services. An example is the £2.1 billion Sustainability and Transformation fund set up to stabilise NHS finances, in tandem with higher rates of efficiency growth, and to provide funding for transition to more effective models of care. Many of this fund’s uses impact on hospital care and require good evidence and understanding of hospital activity – at a local level but within a national context as provided by HES. Another example is the redesign of urgent and emergency care (UEC) services to cope with the increasing demand on A&E departments and emergency admissions. A specific part of that is the development of new indicators to monitor UEC effectiveness due in October 2016. Some of this analysis will be for internal management purposes and key outputs will be published in various forms (see examples below). NHS England access to record level data is necessary to devise appropriate aggregations eg of activity relating to diseases or groups of operations, to calculate statistics such as median length of stay, to break down total counts to understand their components and to analyse connected activity such as A&E attendance and emergency admission. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users who form the analytics team, will have access to record level data downloaded from the HDIS system. Following completion of the analysis the record level data will be securely destroyed.	NHS England users access HDIS via a secure portal from encrypted laptops and desktops based in a number of locations. These are encrypted by Bitlocker to the AES-256 standard These devices have the VMware software necessary to access HDIS, but not HDIS itself, which remains always in the NHS Digital remote environment. Access is not possible without additionally having a user id, password and RSA token. NHS Digital grant NHS England the ability to carry out analysis on HES data using the SAS Enterprise Guide analytical tool. Data are viewed and analysed remotely via this secure means. NHS England users also have the ability to locally download record level results, outputs and extracts from the HDIS system. Such downloads are stored and processed securely on NHS England servers (or equivalent for Strategic Clinical Network users who are legally part of NHS England). These are generally in the form of tables for further analysis, aggregation, standardisation and computation or for inclusion in briefing, documents, models and tools. Record level extracts are only required for small numbers of cases where further manipulation is required eg to understand how episodes relate to the same spell, pathway or patient. No patient identifiable data are provided and records are not linked to any other source. The data are not used for commercial use. For all outputs small numbers are suppressed in line with the HES Analysis Guide. Any unsuppressed tables are stored and, where necessary, shared securely with colleagues involved in the analysis of results and the unsuppressed data will not be shared with third parties. The data will be processed for the purposes described in this document. Most tables extracted from HDIS are aggregated to CCG, Hospital provider or national level, but further breakdowns may be required eg to build geographies based on Local Authority District or GP practices. HES data may be analysed at aggregate level with other data sources, especially resident or GP registered populations to create activity rates. NHS England have 30 analyst users who are part of the agreed 50 licenses (together with DH, no DH users have access to the HDIS system under the terms of this agreement) that are covered by the GIA arrangement.	NHS England use HES data on an ongoing basis for management purposes, for internal review, for information and tools to support the commissioning and provision of NHS services and in publications relevant to NHS England business plan and the objectives of NHS England mandate. The following examples illustrate the ongoing use of the HES data and outputs expected in the coming year: • Enumerating activity for specialised commissioning; • Reviewing trends in diagnostic testing to improve early cancer detection; • Validating the claims of New Care Model vanguards to improve eg emergency admission rates and bed days or A&E attendance for children and young people; • Supporting the Maternity Transformation Programme (currently being launched) to deliver safer, more personalised care; • Contributing to the Right Care Commissioning for Value packs to help CCGs do efficient and effective commissioning; • Informing the Congenital Heart Disease Review to secure the best outcomes for patients; • Providing baselines for the CCG Improvement and Assessment Framework for performance monitoring; • Producing hospital related indicators for the Primary Care Dashboard for GP Practices; • Refining the formula for the Mental health tariff; • Developing system wide indicators for Urgent and Emergency Care Networks, to implement from 2017. In addition, NHS England users will analyse HES data to contribute to many other workstreams and handle briefing requests on an ad hoc basis. Each item is separately commissioned and target dates are set during the programme. Examples of external-facing uses of the data are given in the benefits section below. Recent examples of internal and unpublished briefing, tools and analysis are as follows. • Internal analysis paper investigating the appropriate metric for calculating bed-days; • High-level briefing on trends in the use of the independent sector by the NHS; • Dashboard showing variations in endoscopy provision across England; • Briefing paper assessing evidence for the impact of a New Care Models vanguard. The data are used for internal purposes such as briefing and specialised commissioning, for advising NHS organisations such as Trusts and CCGs or for wider publication such as in the examples below. The data are not used for commercial use. Small numbers are suppressed in line with the HES Analysis Guide.	NHS England has an objective to allow everyone to have greater control of their health and wellbeing, support individuals to live longer, healthier lives by the provision of high quality health and care services that are compassionate, inclusive and constantly-improving. The vision for that was set out in the NHS Five Year Forward View, published in 2014. NHS England's effectiveness in achieving this is summarised in their Annual Report: https://www.england.nhs.uk/wp-content/uploads/2016/07/nhse-annual-rep-201516.pdf Public examples of earlier work drawing on HES outputs that have benefitted patients and Health communities include: • The route map for Urgent and Emergency Care that includes the piloting of outcome metrics to demonstrate improvements for patients: https://www.england.nhs.uk/wp-content/uploads/2015/11/item5-board-20-11-15.pdf. Without these outcome metrics, there is no measure of success for the initiatives implemented. • Publishing a breach rate for mixed sex accommodation that uses HES data in combination with NHS England data: http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/. Without this breach rate there would be no accountability and patients would continue to suffer the problems of mixed sex wards. • Tools helping the NHS (and the public) to review and address variation such as the Diagnostic Atlas of Variation: http://www.rightcare.nhs.uk/index.php/atlas/diagnostics-the-nhs-atlas-of-variation-in-diagnostics-services/. Without these tools, local health communities may invest in services that do not provide maximum benefit for patients. • The latest stages of the NHS cancer strategy that include work on improving diagnostic test capacity (drawing on HES analysis): https://www.england.nhs.uk/wp-content/uploads/2016/05/cancer-strategy.pdf. Without this information, there may be insufficient resource put in place to meet demand for cancer diagnostics, leading to worse outcomes.
NHS ENGLAND	NHS ENGLAND	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS England supports across a wide spectrum of responsibilities to support Health and Social care within England access to HDIS is required to support this for the following areas; - commissioning - policy - finance - economic development - research and analysis The above all assist NHS England in its aim to create the culture and conditions for health and care services and staff to deliver the highest standard of care and ensure that valuable public resources are used effectively to get the best outcomes for individuals, communities and society for now and for future generations. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014. HDIS users are based across the organisation and their access is from the secure NHS England environment. They help NHS England to oversee the delivery of NHS funded services and the continuous improvements to the quality of treatment and care by using HES data to inform, target, strategies, monitor, benchmark, cross-check and plan services. An example is the £2.1 billion Sustainability and Transformation fund set up to stabilise NHS finances, in tandem with higher rates of efficiency growth, and to provide funding for transition to more effective models of care. Many of this fund’s uses impact on hospital care and require good evidence and understanding of hospital activity – at a local level but within a national context as provided by HES. Another example is the redesign of urgent and emergency care (UEC) services to cope with the increasing demand on A&E departments and emergency admissions. A specific part of that is the development of new indicators to monitor UEC effectiveness due in October 2016. Some of this analysis will be for internal management purposes and key outputs will be published in various forms (see examples below). NHS England access to record level data is necessary to devise appropriate aggregations eg of activity relating to diseases or groups of operations, to calculate statistics such as median length of stay, to break down total counts to understand their components and to analyse connected activity such as A&E attendance and emergency admission. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users who form the analytics team, will have access to record level data downloaded from the HDIS system. Following completion of the analysis the record level data will be securely destroyed.	NHS England users access HDIS via a secure portal from encrypted laptops and desktops based in a number of locations. These are encrypted by Bitlocker to the AES-256 standard These devices have the VMware software necessary to access HDIS, but not HDIS itself, which remains always in the NHS Digital remote environment. Access is not possible without additionally having a user id, password and RSA token. NHS Digital grant NHS England the ability to carry out analysis on HES data using the SAS Enterprise Guide analytical tool. Data are viewed and analysed remotely via this secure means. NHS England users also have the ability to locally download record level results, outputs and extracts from the HDIS system. Such downloads are stored and processed securely on NHS England servers (or equivalent for Strategic Clinical Network users who are legally part of NHS England). These are generally in the form of tables for further analysis, aggregation, standardisation and computation or for inclusion in briefing, documents, models and tools. Record level extracts are only required for small numbers of cases where further manipulation is required eg to understand how episodes relate to the same spell, pathway or patient. No patient identifiable data are provided and records are not linked to any other source. The data are not used for commercial use. For all outputs small numbers are suppressed in line with the HES Analysis Guide. Any unsuppressed tables are stored and, where necessary, shared securely with colleagues involved in the analysis of results and the unsuppressed data will not be shared with third parties. The data will be processed for the purposes described in this document. Most tables extracted from HDIS are aggregated to CCG, Hospital provider or national level, but further breakdowns may be required eg to build geographies based on Local Authority District or GP practices. HES data may be analysed at aggregate level with other data sources, especially resident or GP registered populations to create activity rates. NHS England have 30 analyst users who are part of the agreed 50 licenses (together with DH, no DH users have access to the HDIS system under the terms of this agreement) that are covered by the GIA arrangement.	NHS England use HES data on an ongoing basis for management purposes, for internal review, for information and tools to support the commissioning and provision of NHS services and in publications relevant to NHS England business plan and the objectives of NHS England mandate. The following examples illustrate the ongoing use of the HES data and outputs expected in the coming year: • Enumerating activity for specialised commissioning; • Reviewing trends in diagnostic testing to improve early cancer detection; • Validating the claims of New Care Model vanguards to improve eg emergency admission rates and bed days or A&E attendance for children and young people; • Supporting the Maternity Transformation Programme (currently being launched) to deliver safer, more personalised care; • Contributing to the Right Care Commissioning for Value packs to help CCGs do efficient and effective commissioning; • Informing the Congenital Heart Disease Review to secure the best outcomes for patients; • Providing baselines for the CCG Improvement and Assessment Framework for performance monitoring; • Producing hospital related indicators for the Primary Care Dashboard for GP Practices; • Refining the formula for the Mental health tariff; • Developing system wide indicators for Urgent and Emergency Care Networks, to implement from 2017. In addition, NHS England users will analyse HES data to contribute to many other workstreams and handle briefing requests on an ad hoc basis. Each item is separately commissioned and target dates are set during the programme. Examples of external-facing uses of the data are given in the benefits section below. Recent examples of internal and unpublished briefing, tools and analysis are as follows. • Internal analysis paper investigating the appropriate metric for calculating bed-days; • High-level briefing on trends in the use of the independent sector by the NHS; • Dashboard showing variations in endoscopy provision across England; • Briefing paper assessing evidence for the impact of a New Care Models vanguard. The data are used for internal purposes such as briefing and specialised commissioning, for advising NHS organisations such as Trusts and CCGs or for wider publication such as in the examples below. The data are not used for commercial use. Small numbers are suppressed in line with the HES Analysis Guide.	NHS England has an objective to allow everyone to have greater control of their health and wellbeing, support individuals to live longer, healthier lives by the provision of high quality health and care services that are compassionate, inclusive and constantly-improving. The vision for that was set out in the NHS Five Year Forward View, published in 2014. NHS England's effectiveness in achieving this is summarised in their Annual Report: https://www.england.nhs.uk/wp-content/uploads/2016/07/nhse-annual-rep-201516.pdf Public examples of earlier work drawing on HES outputs that have benefitted patients and Health communities include: • The route map for Urgent and Emergency Care that includes the piloting of outcome metrics to demonstrate improvements for patients: https://www.england.nhs.uk/wp-content/uploads/2015/11/item5-board-20-11-15.pdf. Without these outcome metrics, there is no measure of success for the initiatives implemented. • Publishing a breach rate for mixed sex accommodation that uses HES data in combination with NHS England data: http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/. Without this breach rate there would be no accountability and patients would continue to suffer the problems of mixed sex wards. • Tools helping the NHS (and the public) to review and address variation such as the Diagnostic Atlas of Variation: http://www.rightcare.nhs.uk/index.php/atlas/diagnostics-the-nhs-atlas-of-variation-in-diagnostics-services/. Without these tools, local health communities may invest in services that do not provide maximum benefit for patients. • The latest stages of the NHS cancer strategy that include work on improving diagnostic test capacity (drawing on HES analysis): https://www.england.nhs.uk/wp-content/uploads/2016/05/cancer-strategy.pdf. Without this information, there may be insufficient resource put in place to meet demand for cancer diagnostics, leading to worse outcomes.
NHS ENGLAND	NHS ENGLAND	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS England supports across a wide spectrum of responsibilities to support Health and Social care within England access to HDIS is required to support this for the following areas; - commissioning - policy - finance - economic development - research and analysis The above all assist NHS England in its aim to create the culture and conditions for health and care services and staff to deliver the highest standard of care and ensure that valuable public resources are used effectively to get the best outcomes for individuals, communities and society for now and for future generations. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014. HDIS users are based across the organisation and their access is from the secure NHS England environment. They help NHS England to oversee the delivery of NHS funded services and the continuous improvements to the quality of treatment and care by using HES data to inform, target, strategies, monitor, benchmark, cross-check and plan services. An example is the £2.1 billion Sustainability and Transformation fund set up to stabilise NHS finances, in tandem with higher rates of efficiency growth, and to provide funding for transition to more effective models of care. Many of this fund’s uses impact on hospital care and require good evidence and understanding of hospital activity – at a local level but within a national context as provided by HES. Another example is the redesign of urgent and emergency care (UEC) services to cope with the increasing demand on A&E departments and emergency admissions. A specific part of that is the development of new indicators to monitor UEC effectiveness due in October 2016. Some of this analysis will be for internal management purposes and key outputs will be published in various forms (see examples below). NHS England access to record level data is necessary to devise appropriate aggregations eg of activity relating to diseases or groups of operations, to calculate statistics such as median length of stay, to break down total counts to understand their components and to analyse connected activity such as A&E attendance and emergency admission. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users who form the analytics team, will have access to record level data downloaded from the HDIS system. Following completion of the analysis the record level data will be securely destroyed.	NHS England users access HDIS via a secure portal from encrypted laptops and desktops based in a number of locations. These are encrypted by Bitlocker to the AES-256 standard These devices have the VMware software necessary to access HDIS, but not HDIS itself, which remains always in the NHS Digital remote environment. Access is not possible without additionally having a user id, password and RSA token. NHS Digital grant NHS England the ability to carry out analysis on HES data using the SAS Enterprise Guide analytical tool. Data are viewed and analysed remotely via this secure means. NHS England users also have the ability to locally download record level results, outputs and extracts from the HDIS system. Such downloads are stored and processed securely on NHS England servers (or equivalent for Strategic Clinical Network users who are legally part of NHS England). These are generally in the form of tables for further analysis, aggregation, standardisation and computation or for inclusion in briefing, documents, models and tools. Record level extracts are only required for small numbers of cases where further manipulation is required eg to understand how episodes relate to the same spell, pathway or patient. No patient identifiable data are provided and records are not linked to any other source. The data are not used for commercial use. For all outputs small numbers are suppressed in line with the HES Analysis Guide. Any unsuppressed tables are stored and, where necessary, shared securely with colleagues involved in the analysis of results and the unsuppressed data will not be shared with third parties. The data will be processed for the purposes described in this document. Most tables extracted from HDIS are aggregated to CCG, Hospital provider or national level, but further breakdowns may be required eg to build geographies based on Local Authority District or GP practices. HES data may be analysed at aggregate level with other data sources, especially resident or GP registered populations to create activity rates. NHS England have 30 analyst users who are part of the agreed 50 licenses (together with DH, no DH users have access to the HDIS system under the terms of this agreement) that are covered by the GIA arrangement.	NHS England use HES data on an ongoing basis for management purposes, for internal review, for information and tools to support the commissioning and provision of NHS services and in publications relevant to NHS England business plan and the objectives of NHS England mandate. The following examples illustrate the ongoing use of the HES data and outputs expected in the coming year: • Enumerating activity for specialised commissioning; • Reviewing trends in diagnostic testing to improve early cancer detection; • Validating the claims of New Care Model vanguards to improve eg emergency admission rates and bed days or A&E attendance for children and young people; • Supporting the Maternity Transformation Programme (currently being launched) to deliver safer, more personalised care; • Contributing to the Right Care Commissioning for Value packs to help CCGs do efficient and effective commissioning; • Informing the Congenital Heart Disease Review to secure the best outcomes for patients; • Providing baselines for the CCG Improvement and Assessment Framework for performance monitoring; • Producing hospital related indicators for the Primary Care Dashboard for GP Practices; • Refining the formula for the Mental health tariff; • Developing system wide indicators for Urgent and Emergency Care Networks, to implement from 2017. In addition, NHS England users will analyse HES data to contribute to many other workstreams and handle briefing requests on an ad hoc basis. Each item is separately commissioned and target dates are set during the programme. Examples of external-facing uses of the data are given in the benefits section below. Recent examples of internal and unpublished briefing, tools and analysis are as follows. • Internal analysis paper investigating the appropriate metric for calculating bed-days; • High-level briefing on trends in the use of the independent sector by the NHS; • Dashboard showing variations in endoscopy provision across England; • Briefing paper assessing evidence for the impact of a New Care Models vanguard. The data are used for internal purposes such as briefing and specialised commissioning, for advising NHS organisations such as Trusts and CCGs or for wider publication such as in the examples below. The data are not used for commercial use. Small numbers are suppressed in line with the HES Analysis Guide.	NHS England has an objective to allow everyone to have greater control of their health and wellbeing, support individuals to live longer, healthier lives by the provision of high quality health and care services that are compassionate, inclusive and constantly-improving. The vision for that was set out in the NHS Five Year Forward View, published in 2014. NHS England's effectiveness in achieving this is summarised in their Annual Report: https://www.england.nhs.uk/wp-content/uploads/2016/07/nhse-annual-rep-201516.pdf Public examples of earlier work drawing on HES outputs that have benefitted patients and Health communities include: • The route map for Urgent and Emergency Care that includes the piloting of outcome metrics to demonstrate improvements for patients: https://www.england.nhs.uk/wp-content/uploads/2015/11/item5-board-20-11-15.pdf. Without these outcome metrics, there is no measure of success for the initiatives implemented. • Publishing a breach rate for mixed sex accommodation that uses HES data in combination with NHS England data: http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/. Without this breach rate there would be no accountability and patients would continue to suffer the problems of mixed sex wards. • Tools helping the NHS (and the public) to review and address variation such as the Diagnostic Atlas of Variation: http://www.rightcare.nhs.uk/index.php/atlas/diagnostics-the-nhs-atlas-of-variation-in-diagnostics-services/. Without these tools, local health communities may invest in services that do not provide maximum benefit for patients. • The latest stages of the NHS cancer strategy that include work on improving diagnostic test capacity (drawing on HES analysis): https://www.england.nhs.uk/wp-content/uploads/2016/05/cancer-strategy.pdf. Without this information, there may be insufficient resource put in place to meet demand for cancer diagnostics, leading to worse outcomes.
NHS ENGLAND	NHS ENGLAND	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The efficient and equitable allocation of funding to support different services, geographies and patient groups is a fundamental underpinning to the operation of the health service. Without this, the opportunity for patients to access healthcare in line with need would be unequal, and the ability to address inequalities in health would be undermined. The approach to achieving the efficient and equitable allocation of resources has two key steps: first, understanding the current distribution of resources and estimating the relative target distribution; the second is that which would deliver the most efficient and equitable distribution of resources based on the relative need for healthcare services between geographical areas and patient groups. The data that is the subject of this application would be used to develop the target allocation shares for Clinical Commissioning Groups (CCGs) of the national budget for England. The target formula used for allocation of resources for CCGs was developed by the Nuffield Trust (see www.nuffieldtrust.org.uk/our-work/projects/person-based-resource-allocation-pbra) and is the most robust resource allocation methodology we have ever used. Its strength comes from building organisational allocations up from individual level estimates of need for health services, which can exploit interacting information about each patient’s age, gender, area of residence and hospital recorded diagnosis information. These individual level estimates can then be built up in to organisational level estimates; the estimates for individuals themselves are not reliable and are not used (nor published). The Nuffield work was updated for two reasons. First, the original modelling is based on information that is several years old (2007-08 to 2009-10) and we would want to update this to ensure it remains robust. Second the development of commissioning and a more place-based approach is driving a reconsideration of where the boundaries are between different commissioning streams. If the responsibilities of Clinical Commissioning Groups change, the relative distribution of resources that is most efficient will also change. In particular NHS England need to consider how the target allocation would need to change if significant elements of the directly commissioned specialised services became part of CCGs’ funding responsibilities. In summary, therefore, the objective is to estimate the relative need for healthcare services for each CCG’s population and for specialised services at CCG level, based upon modelling the use of healthcare services and diagnoses data. To confirm, the data will only be used for the development of a formula for target funding allocations for each Clinical Commissioning Group and for specialised services currently commissioned by NHS England and to then follow up on queries submitted following publication.	The requested data and linkage will be used to create a record for each individual in England. The record will include for four years their admitted care, outpatient care, A&E attendances and critical care, or alternatively that they have received no hospital care. The record will also include diagnostic information from SUS PBR. This data set will be held solely by NHS Digital on their IT systems. Access to the data will be via secure virtual access using tokens and individual login details on NHS England computers. Data will be accessed by a limited number of authorised individuals from NHS England who are all substantive employees. Processing took the form of statistical modelling of individual patient record level data. The modelling had annual estimated expenditure for each patient as the dependent variable and the patient’s age, diagnoses, and characteristics of the local area where the patient resides as the explanatory variables. The characteristics of the local area where the patient resides are publicly available data from e.g. the 2011 Population Census. Other data was linked to the data set, but only organisational level data (eg: QOF) or reference data (eg: organisational name against an organization code). No additional record level data was linked to the dataset. The only data from this work taken away to NHS England’s premises was a) the coefficients from the regression modelling and diagnostic test results of the robustness of the modelling; b) the estimated need per head by age-gender group by GP practice; c) the estimated proportion of need per head by age-gender group by CCG by groups of specialised services (for changes in CCGs’ responsibilities); d) aggregate level descriptive data eg the number of people receiving treatment not registered with a GP. No record level data was taken away from the HSCIC Secure Data facility, and thus only aggregate or organisation level statistical information will be published. The results of the analysis performed on this data are published here: https://www.england.nhs.uk/2016/04/allocations-tech-guide-16-17/ The work has allowed the publication of 5 years of allocations to CCGs. All persons accessing the data are substantive employees of NHS England.	The outputs were predicted average need related expenditure per head for health care services for each age/gender group for each GP practice in England, and at CCG level for specialised services. The outputs were the coefficients from the regression model, and also these multiplied by the values of the explanatory variables for each patient, the products of which are then aggregated to give average need per head by age/gender group by GP practice and for specialised services by CCG. The coefficients from the regression models were published in NHS England’s technical guide to allocations. The predicted average need per head by age/gender group by GP practice were published in the technical guide. The average need per head was suppressed in the publication where they apply to small numbers in line with the HES analysis guide. These will be equivalent to the previous research by Nuffield Trust published in the Excel file C Need per head (General and Acute), worksheets GP practice Need Values and Nuffield Model Variables, at: http://www.england.nhs.uk/2014/03/27/allocations-tech-guide/ The values of the explanatory variables or record level data were not published. The data are requested for retention for five years to allow NHS England to respond to queries on the allocations, and re-model for future changes in CCG responsibilities arising with co-commissioning. The further analysis NHS England want to perform in 2017/18 is to investigate a query they have had from a CCG regarding their needs weighting from the model. This will involve looking at some of the inputs to the model from that geographical area and comparing them to other areas. This will be done through access to the secure connection to the data stored in the NHS Digital facility. NHS England is not permitted to transfer any of the data out of the NHS Digital facility, nor is it permitted to attempt to do so. The outputs will be qualitative descriptions of what have been found when comparing the CCG’s input data with other areas. This will allow NHS England to understand whether any mistakes were made in the calculation of the formula and therefore whether the formula may need to be refined to ensure that allocations are as fair as possible in relation to need; or whether there were some local data issues that skewed the output for that particular CCG; or whether in fact there is no issue at all. NHS England need to be able to confirm that resources have been allocated in accordance with the objectives of providing equal opportunity of access to healthcare for equal need, and addressing health inequalities. Without being able to re-look at the original data used for current allocations NHS England cannot be confident that allocations have been carried out in the most equitable and efficient manner possible, and cannot make changes to improve the process in future. In addition, in an extreme scenario, should NHS England find any significant mistakes in the outputs then certain CCGs could theoretically be eligible for compensation which would have a direct impact on patient care. The predicted need per head was used to calculate CCG target funding allocations, by combining with population sizes and other components of the target formula which were not calculated from this requested data set.	An updated and improved formula has allowed more equitable funding of CCGs across England and thereby supported more equitable access by patients to NHS health care services. The benefits of equitable allocations are difficult to quantify, but the size of the budget being allocated (over £65bn) is sufficiently large that a small marginal improvement would have significant absolute benefits. A recent longitudinal study by Barr et al (www.bmj.com/content/348/bmj.g3231.reprint) has provided the clearest evidence so far of the impact of additional resources on health status, demonstrating a link between a reduction in deaths amenable to healthcare and increased investment. This demonstrates that an effective distribution of resources in line with need should, if followed by appropriate commissioning, be expected to deliver improvements in healthcare outcomes for individuals.
NHS ENGLAND	NHS ENGLAND	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The efficient and equitable allocation of funding to support different services, geographies and patient groups is a fundamental underpinning to the operation of the health service. Without this, the opportunity for patients to access healthcare in line with need would be unequal, and the ability to address inequalities in health would be undermined. The approach to achieving the efficient and equitable allocation of resources has two key steps: first, understanding the current distribution of resources and estimating the relative target distribution; the second is that which would deliver the most efficient and equitable distribution of resources based on the relative need for healthcare services between geographical areas and patient groups. The data that is the subject of this application would be used to develop the target allocation shares for Clinical Commissioning Groups (CCGs) of the national budget for England. The target formula used for allocation of resources for CCGs was developed by the Nuffield Trust (see www.nuffieldtrust.org.uk/our-work/projects/person-based-resource-allocation-pbra) and is the most robust resource allocation methodology we have ever used. Its strength comes from building organisational allocations up from individual level estimates of need for health services, which can exploit interacting information about each patient’s age, gender, area of residence and hospital recorded diagnosis information. These individual level estimates can then be built up in to organisational level estimates; the estimates for individuals themselves are not reliable and are not used (nor published). The Nuffield work was updated for two reasons. First, the original modelling is based on information that is several years old (2007-08 to 2009-10) and we would want to update this to ensure it remains robust. Second the development of commissioning and a more place-based approach is driving a reconsideration of where the boundaries are between different commissioning streams. If the responsibilities of Clinical Commissioning Groups change, the relative distribution of resources that is most efficient will also change. In particular NHS England need to consider how the target allocation would need to change if significant elements of the directly commissioned specialised services became part of CCGs’ funding responsibilities. In summary, therefore, the objective is to estimate the relative need for healthcare services for each CCG’s population and for specialised services at CCG level, based upon modelling the use of healthcare services and diagnoses data. To confirm, the data will only be used for the development of a formula for target funding allocations for each Clinical Commissioning Group and for specialised services currently commissioned by NHS England and to then follow up on queries submitted following publication.	The requested data and linkage will be used to create a record for each individual in England. The record will include for four years their admitted care, outpatient care, A&E attendances and critical care, or alternatively that they have received no hospital care. The record will also include diagnostic information from SUS PBR. This data set will be held solely by NHS Digital on their IT systems. Access to the data will be via secure virtual access using tokens and individual login details on NHS England computers. Data will be accessed by a limited number of authorised individuals from NHS England who are all substantive employees. Processing took the form of statistical modelling of individual patient record level data. The modelling had annual estimated expenditure for each patient as the dependent variable and the patient’s age, diagnoses, and characteristics of the local area where the patient resides as the explanatory variables. The characteristics of the local area where the patient resides are publicly available data from e.g. the 2011 Population Census. Other data was linked to the data set, but only organisational level data (eg: QOF) or reference data (eg: organisational name against an organization code). No additional record level data was linked to the dataset. The only data from this work taken away to NHS England’s premises was a) the coefficients from the regression modelling and diagnostic test results of the robustness of the modelling; b) the estimated need per head by age-gender group by GP practice; c) the estimated proportion of need per head by age-gender group by CCG by groups of specialised services (for changes in CCGs’ responsibilities); d) aggregate level descriptive data eg the number of people receiving treatment not registered with a GP. No record level data was taken away from the HSCIC Secure Data facility, and thus only aggregate or organisation level statistical information will be published. The results of the analysis performed on this data are published here: https://www.england.nhs.uk/2016/04/allocations-tech-guide-16-17/ The work has allowed the publication of 5 years of allocations to CCGs. All persons accessing the data are substantive employees of NHS England.	The outputs were predicted average need related expenditure per head for health care services for each age/gender group for each GP practice in England, and at CCG level for specialised services. The outputs were the coefficients from the regression model, and also these multiplied by the values of the explanatory variables for each patient, the products of which are then aggregated to give average need per head by age/gender group by GP practice and for specialised services by CCG. The coefficients from the regression models were published in NHS England’s technical guide to allocations. The predicted average need per head by age/gender group by GP practice were published in the technical guide. The average need per head was suppressed in the publication where they apply to small numbers in line with the HES analysis guide. These will be equivalent to the previous research by Nuffield Trust published in the Excel file C Need per head (General and Acute), worksheets GP practice Need Values and Nuffield Model Variables, at: http://www.england.nhs.uk/2014/03/27/allocations-tech-guide/ The values of the explanatory variables or record level data were not published. The data are requested for retention for five years to allow NHS England to respond to queries on the allocations, and re-model for future changes in CCG responsibilities arising with co-commissioning. The further analysis NHS England want to perform in 2017/18 is to investigate a query they have had from a CCG regarding their needs weighting from the model. This will involve looking at some of the inputs to the model from that geographical area and comparing them to other areas. This will be done through access to the secure connection to the data stored in the NHS Digital facility. NHS England is not permitted to transfer any of the data out of the NHS Digital facility, nor is it permitted to attempt to do so. The outputs will be qualitative descriptions of what have been found when comparing the CCG’s input data with other areas. This will allow NHS England to understand whether any mistakes were made in the calculation of the formula and therefore whether the formula may need to be refined to ensure that allocations are as fair as possible in relation to need; or whether there were some local data issues that skewed the output for that particular CCG; or whether in fact there is no issue at all. NHS England need to be able to confirm that resources have been allocated in accordance with the objectives of providing equal opportunity of access to healthcare for equal need, and addressing health inequalities. Without being able to re-look at the original data used for current allocations NHS England cannot be confident that allocations have been carried out in the most equitable and efficient manner possible, and cannot make changes to improve the process in future. In addition, in an extreme scenario, should NHS England find any significant mistakes in the outputs then certain CCGs could theoretically be eligible for compensation which would have a direct impact on patient care. The predicted need per head was used to calculate CCG target funding allocations, by combining with population sizes and other components of the target formula which were not calculated from this requested data set.	An updated and improved formula has allowed more equitable funding of CCGs across England and thereby supported more equitable access by patients to NHS health care services. The benefits of equitable allocations are difficult to quantify, but the size of the budget being allocated (over £65bn) is sufficiently large that a small marginal improvement would have significant absolute benefits. A recent longitudinal study by Barr et al (www.bmj.com/content/348/bmj.g3231.reprint) has provided the clearest evidence so far of the impact of additional resources on health status, demonstrating a link between a reduction in deaths amenable to healthcare and increased investment. This demonstrates that an effective distribution of resources in line with need should, if followed by appropriate commissioning, be expected to deliver improvements in healthcare outcomes for individuals.
NHS ENGLAND	NHS ENGLAND	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The efficient and equitable allocation of funding to support different services, geographies and patient groups is a fundamental underpinning to the operation of the health service. Without this, the opportunity for patients to access healthcare in line with need would be unequal, and the ability to address inequalities in health would be undermined. The approach to achieving the efficient and equitable allocation of resources has two key steps: first, understanding the current distribution of resources and estimating the relative target distribution; the second is that which would deliver the most efficient and equitable distribution of resources based on the relative need for healthcare services between geographical areas and patient groups. The data that is the subject of this application would be used to develop the target allocation shares for Clinical Commissioning Groups (CCGs) of the national budget for England. The target formula used for allocation of resources for CCGs was developed by the Nuffield Trust (see www.nuffieldtrust.org.uk/our-work/projects/person-based-resource-allocation-pbra) and is the most robust resource allocation methodology we have ever used. Its strength comes from building organisational allocations up from individual level estimates of need for health services, which can exploit interacting information about each patient’s age, gender, area of residence and hospital recorded diagnosis information. These individual level estimates can then be built up in to organisational level estimates; the estimates for individuals themselves are not reliable and are not used (nor published). The Nuffield work was updated for two reasons. First, the original modelling is based on information that is several years old (2007-08 to 2009-10) and we would want to update this to ensure it remains robust. Second the development of commissioning and a more place-based approach is driving a reconsideration of where the boundaries are between different commissioning streams. If the responsibilities of Clinical Commissioning Groups change, the relative distribution of resources that is most efficient will also change. In particular NHS England need to consider how the target allocation would need to change if significant elements of the directly commissioned specialised services became part of CCGs’ funding responsibilities. In summary, therefore, the objective is to estimate the relative need for healthcare services for each CCG’s population and for specialised services at CCG level, based upon modelling the use of healthcare services and diagnoses data. To confirm, the data will only be used for the development of a formula for target funding allocations for each Clinical Commissioning Group and for specialised services currently commissioned by NHS England and to then follow up on queries submitted following publication.	The requested data and linkage will be used to create a record for each individual in England. The record will include for four years their admitted care, outpatient care, A&E attendances and critical care, or alternatively that they have received no hospital care. The record will also include diagnostic information from SUS PBR. This data set will be held solely by NHS Digital on their IT systems. Access to the data will be via secure virtual access using tokens and individual login details on NHS England computers. Data will be accessed by a limited number of authorised individuals from NHS England who are all substantive employees. Processing took the form of statistical modelling of individual patient record level data. The modelling had annual estimated expenditure for each patient as the dependent variable and the patient’s age, diagnoses, and characteristics of the local area where the patient resides as the explanatory variables. The characteristics of the local area where the patient resides are publicly available data from e.g. the 2011 Population Census. Other data was linked to the data set, but only organisational level data (eg: QOF) or reference data (eg: organisational name against an organization code). No additional record level data was linked to the dataset. The only data from this work taken away to NHS England’s premises was a) the coefficients from the regression modelling and diagnostic test results of the robustness of the modelling; b) the estimated need per head by age-gender group by GP practice; c) the estimated proportion of need per head by age-gender group by CCG by groups of specialised services (for changes in CCGs’ responsibilities); d) aggregate level descriptive data eg the number of people receiving treatment not registered with a GP. No record level data was taken away from the HSCIC Secure Data facility, and thus only aggregate or organisation level statistical information will be published. The results of the analysis performed on this data are published here: https://www.england.nhs.uk/2016/04/allocations-tech-guide-16-17/ The work has allowed the publication of 5 years of allocations to CCGs. All persons accessing the data are substantive employees of NHS England.	The outputs were predicted average need related expenditure per head for health care services for each age/gender group for each GP practice in England, and at CCG level for specialised services. The outputs were the coefficients from the regression model, and also these multiplied by the values of the explanatory variables for each patient, the products of which are then aggregated to give average need per head by age/gender group by GP practice and for specialised services by CCG. The coefficients from the regression models were published in NHS England’s technical guide to allocations. The predicted average need per head by age/gender group by GP practice were published in the technical guide. The average need per head was suppressed in the publication where they apply to small numbers in line with the HES analysis guide. These will be equivalent to the previous research by Nuffield Trust published in the Excel file C Need per head (General and Acute), worksheets GP practice Need Values and Nuffield Model Variables, at: http://www.england.nhs.uk/2014/03/27/allocations-tech-guide/ The values of the explanatory variables or record level data were not published. The data are requested for retention for five years to allow NHS England to respond to queries on the allocations, and re-model for future changes in CCG responsibilities arising with co-commissioning. The further analysis NHS England want to perform in 2017/18 is to investigate a query they have had from a CCG regarding their needs weighting from the model. This will involve looking at some of the inputs to the model from that geographical area and comparing them to other areas. This will be done through access to the secure connection to the data stored in the NHS Digital facility. NHS England is not permitted to transfer any of the data out of the NHS Digital facility, nor is it permitted to attempt to do so. The outputs will be qualitative descriptions of what have been found when comparing the CCG’s input data with other areas. This will allow NHS England to understand whether any mistakes were made in the calculation of the formula and therefore whether the formula may need to be refined to ensure that allocations are as fair as possible in relation to need; or whether there were some local data issues that skewed the output for that particular CCG; or whether in fact there is no issue at all. NHS England need to be able to confirm that resources have been allocated in accordance with the objectives of providing equal opportunity of access to healthcare for equal need, and addressing health inequalities. Without being able to re-look at the original data used for current allocations NHS England cannot be confident that allocations have been carried out in the most equitable and efficient manner possible, and cannot make changes to improve the process in future. In addition, in an extreme scenario, should NHS England find any significant mistakes in the outputs then certain CCGs could theoretically be eligible for compensation which would have a direct impact on patient care. The predicted need per head was used to calculate CCG target funding allocations, by combining with population sizes and other components of the target formula which were not calculated from this requested data set.	An updated and improved formula has allowed more equitable funding of CCGs across England and thereby supported more equitable access by patients to NHS health care services. The benefits of equitable allocations are difficult to quantify, but the size of the budget being allocated (over £65bn) is sufficiently large that a small marginal improvement would have significant absolute benefits. A recent longitudinal study by Barr et al (www.bmj.com/content/348/bmj.g3231.reprint) has provided the clearest evidence so far of the impact of additional resources on health status, demonstrating a link between a reduction in deaths amenable to healthcare and increased investment. This demonstrates that an effective distribution of resources in line with need should, if followed by appropriate commissioning, be expected to deliver improvements in healthcare outcomes for individuals.
NHS ENGLAND	NHS ENGLAND	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The efficient and equitable allocation of funding to support different services, geographies and patient groups is a fundamental underpinning to the operation of the health service. Without this, the opportunity for patients to access healthcare in line with need would be unequal, and the ability to address inequalities in health would be undermined. The approach to achieving the efficient and equitable allocation of resources has two key steps: first, understanding the current distribution of resources and estimating the relative target distribution; the second is that which would deliver the most efficient and equitable distribution of resources based on the relative need for healthcare services between geographical areas and patient groups. The data that is the subject of this application would be used to develop the target allocation shares for Clinical Commissioning Groups (CCGs) of the national budget for England. The target formula used for allocation of resources for CCGs was developed by the Nuffield Trust (see www.nuffieldtrust.org.uk/our-work/projects/person-based-resource-allocation-pbra) and is the most robust resource allocation methodology we have ever used. Its strength comes from building organisational allocations up from individual level estimates of need for health services, which can exploit interacting information about each patient’s age, gender, area of residence and hospital recorded diagnosis information. These individual level estimates can then be built up in to organisational level estimates; the estimates for individuals themselves are not reliable and are not used (nor published). The Nuffield work was updated for two reasons. First, the original modelling is based on information that is several years old (2007-08 to 2009-10) and we would want to update this to ensure it remains robust. Second the development of commissioning and a more place-based approach is driving a reconsideration of where the boundaries are between different commissioning streams. If the responsibilities of Clinical Commissioning Groups change, the relative distribution of resources that is most efficient will also change. In particular NHS England need to consider how the target allocation would need to change if significant elements of the directly commissioned specialised services became part of CCGs’ funding responsibilities. In summary, therefore, the objective is to estimate the relative need for healthcare services for each CCG’s population and for specialised services at CCG level, based upon modelling the use of healthcare services and diagnoses data. To confirm, the data will only be used for the development of a formula for target funding allocations for each Clinical Commissioning Group and for specialised services currently commissioned by NHS England and to then follow up on queries submitted following publication.	The requested data and linkage will be used to create a record for each individual in England. The record will include for four years their admitted care, outpatient care, A&E attendances and critical care, or alternatively that they have received no hospital care. The record will also include diagnostic information from SUS PBR. This data set will be held solely by NHS Digital on their IT systems. Access to the data will be via secure virtual access using tokens and individual login details on NHS England computers. Data will be accessed by a limited number of authorised individuals from NHS England who are all substantive employees. Processing took the form of statistical modelling of individual patient record level data. The modelling had annual estimated expenditure for each patient as the dependent variable and the patient’s age, diagnoses, and characteristics of the local area where the patient resides as the explanatory variables. The characteristics of the local area where the patient resides are publicly available data from e.g. the 2011 Population Census. Other data was linked to the data set, but only organisational level data (eg: QOF) or reference data (eg: organisational name against an organization code). No additional record level data was linked to the dataset. The only data from this work taken away to NHS England’s premises was a) the coefficients from the regression modelling and diagnostic test results of the robustness of the modelling; b) the estimated need per head by age-gender group by GP practice; c) the estimated proportion of need per head by age-gender group by CCG by groups of specialised services (for changes in CCGs’ responsibilities); d) aggregate level descriptive data eg the number of people receiving treatment not registered with a GP. No record level data was taken away from the HSCIC Secure Data facility, and thus only aggregate or organisation level statistical information will be published. The results of the analysis performed on this data are published here: https://www.england.nhs.uk/2016/04/allocations-tech-guide-16-17/ The work has allowed the publication of 5 years of allocations to CCGs. All persons accessing the data are substantive employees of NHS England.	The outputs were predicted average need related expenditure per head for health care services for each age/gender group for each GP practice in England, and at CCG level for specialised services. The outputs were the coefficients from the regression model, and also these multiplied by the values of the explanatory variables for each patient, the products of which are then aggregated to give average need per head by age/gender group by GP practice and for specialised services by CCG. The coefficients from the regression models were published in NHS England’s technical guide to allocations. The predicted average need per head by age/gender group by GP practice were published in the technical guide. The average need per head was suppressed in the publication where they apply to small numbers in line with the HES analysis guide. These will be equivalent to the previous research by Nuffield Trust published in the Excel file C Need per head (General and Acute), worksheets GP practice Need Values and Nuffield Model Variables, at: http://www.england.nhs.uk/2014/03/27/allocations-tech-guide/ The values of the explanatory variables or record level data were not published. The data are requested for retention for five years to allow NHS England to respond to queries on the allocations, and re-model for future changes in CCG responsibilities arising with co-commissioning. The further analysis NHS England want to perform in 2017/18 is to investigate a query they have had from a CCG regarding their needs weighting from the model. This will involve looking at some of the inputs to the model from that geographical area and comparing them to other areas. This will be done through access to the secure connection to the data stored in the NHS Digital facility. NHS England is not permitted to transfer any of the data out of the NHS Digital facility, nor is it permitted to attempt to do so. The outputs will be qualitative descriptions of what have been found when comparing the CCG’s input data with other areas. This will allow NHS England to understand whether any mistakes were made in the calculation of the formula and therefore whether the formula may need to be refined to ensure that allocations are as fair as possible in relation to need; or whether there were some local data issues that skewed the output for that particular CCG; or whether in fact there is no issue at all. NHS England need to be able to confirm that resources have been allocated in accordance with the objectives of providing equal opportunity of access to healthcare for equal need, and addressing health inequalities. Without being able to re-look at the original data used for current allocations NHS England cannot be confident that allocations have been carried out in the most equitable and efficient manner possible, and cannot make changes to improve the process in future. In addition, in an extreme scenario, should NHS England find any significant mistakes in the outputs then certain CCGs could theoretically be eligible for compensation which would have a direct impact on patient care. The predicted need per head was used to calculate CCG target funding allocations, by combining with population sizes and other components of the target formula which were not calculated from this requested data set.	An updated and improved formula has allowed more equitable funding of CCGs across England and thereby supported more equitable access by patients to NHS health care services. The benefits of equitable allocations are difficult to quantify, but the size of the budget being allocated (over £65bn) is sufficiently large that a small marginal improvement would have significant absolute benefits. A recent longitudinal study by Barr et al (www.bmj.com/content/348/bmj.g3231.reprint) has provided the clearest evidence so far of the impact of additional resources on health status, demonstrating a link between a reduction in deaths amenable to healthcare and increased investment. This demonstrates that an effective distribution of resources in line with need should, if followed by appropriate commissioning, be expected to deliver improvements in healthcare outcomes for individuals.
NHS ENGLAND	NHS ENGLAND	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The primary purpose of the flow is for production of Official Statistics, answering Parliamentary Questions (PQs) and media queries, which are the current responsibility of NHS England as subject matter experts. In addition, NHS England will use the data for jointly assessing and addressing data quality problems with HSCIC. Access to pseudonymised record level DID data will also enable NHS England: • To perform better analysis of cancer pathways by indicating where, what and when imaging takes place in the pathway • To allow Public Health England to calculate more accurate estimates of the distribution of individual radiation dose estimates from medical exposures • To enable analysis of demographic and geographic variation in access to diagnostic imaging tests • To provide detailed national data on trends and patterns in NHS imaging to demonstrate how expensive equipment and trained workforce are deployed and support capacity planning • To discontinue the existing annual KH12 dataset and reduce burden on providers • To understand and influence issues around delays in access and turnaround times for tests (including analysis of median periods and distributions) • To provide more detailed national data than is otherwise available on test type (modality), body site of test and patient demographics, which can reveal the impact of initiatives to improve outcomes for patients by influencing the type, timing and number of tests • To allow benchmarking in the rate of provision of diagnostic tests overall and in GPs’ direct access to tests, to encourage increased use of tests leading to earlier diagnosis and hence improved outcomes • To inform accreditation processes for imaging departments through the UK Imaging Services Accreditation Scheme and the assessment of imaging services by the Care Quality Commission. • To inform work on development of accurate tariffs for all diagnostic imaging tests	NHS England currently hold peudonymised DID data from April 2012 onward . The DID data received within this agreement will be added to the DID data already held by NHS England which is used to create monthly and annual Official Statistics publications. DID data will be provided to NHS England on a monthly basis. Each new months’ data is appended to the existing dataset until all files for a financial year have been published. Data is added to the database on 4th of each month and a report is normally published around the third Thursday of the month. NHS England do not hold any identifiable DID data. NHS England will not link DID data to any other data set. All individuals with access to the record level data are employees of NHS England and no third party will have have access to the record level DID data.	Aggregated data is produced on a monthly basis, using accumulated annual figures, with small numbers suppressed in line with the HES analysis guide. This is published as an official statistic, conforming to National Statistics protocols on a public website https://www.england.nhs.uk/statistics/statistical-work-areas/diagnostic-imaging-dataset/ In additional to production of the official statistics, NHS England will use the DID data to produce ad-hoc reports and analyses for the purposes outlined in the objective for processing section. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide. No third party will have access to any record level DID data. Outputs already produced Official statistics from the Diagnostic Imaging Dataset (DID) have been published by NHS England (previously Dept of Health) monthly since 2012-13. In addition, annual reports and additional analyses have been published for 2012-13 to 2014-15. Key statistics include: • Number of diagnostic tests performed • Period from referral to test • Period from test to the test report being issued These measures are reported for nine key modalities including X-ray, Ultrasound, CT and MRI scans. In addition, data are published for a subset of tests that are particularly used to identify or discount a diagnosis of cancer. These statistics are published for England and by Provider on a monthly basis and additionally by Commissioner on an annual basis. They are accompanied by information on data quality, coverage and completeness. Additional annual analyses include: • Annual reports incorporating maps and additional analysis by age, sex, referral source and Provider • Annual technical reports that further explain and describe the data collected • Standardised imaging rates by CCG, showing the variation in provision • Supplementary information on other modalities • A comparison of 2013-14 DID imaging activity with other data sources: DM01 and KH12. In addition to the material published on the NHS England web pages, NHS England produce ad hoc analyses to respond to queries raised by our clinical or policy contacts and others via the contact address did@dh.gsi.gov.uk. Examples of the outputs and associated benefits of these analyses include: • Rates of CT virtual Colonoscopy and Barium enema, which were compared with endoscopy rates (from HES) to show areas of best practice • Cardiac imaging comparisons, showing relative proportions of CT and MRI activity • Analysis by day of the week, to inform the debate around 7-day services • Evaluation with CRUK of ‘Be Clear on Cancer’ initiatives such as for ‘Blood in pee’ and Lung cancer, by demonstrating increased diagnostic activity in periods and areas of the publicity campaigns • Usage of individual NICIP or SNOMED CT codes, to review changes in coding practice • Additional analysis to compare DID waiting times with DM01, to investigate delays around diagnostics • Contributions for consideration or use in the Diagnostic Atlas of Variation published by Rightcare at http://www.rightcare.nhs.uk/index.php/atlas/diagnostics-the-nhs-atlas-of-variation-in-diagnostics-services/ • Data quality analyses, to work together with HSCIC to improve the completeness and usability of DID.	NHS England will utilise DID data to continuously: • Monitor and improve diagnostic imaging services, by measuring access to imaging services • Improve cancer survival rates by reducing referral to treatment times and diagnosing cancers earlier • Reduce unnecessary exposure to radiation by monitoring compliance with clinical guidelines Benefits achieved to date The reported measures for nine key modalities including X-ray, Ultrasound, CT and MRI scans. This provides more information on NHS provision of these services than any other resource and is the only source of national information on some modalities. The trends and patterns of provision demonstrate where there is scope for improving the early of diagnosis of cancer, in particular highlighting the share of referrals made by GPs.
NHS ENGLAND	NHS ENGLAND	Episode and Spell level grouper results; underlying patient level data.	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	To inform the decision making process for determination of the scope and structure of the future Grouper Products
NHS ENGLAND	NHS ENGLAND	Monthly Subscription Assuring Transformation	Identifiable	Sensitive	Health and Social Care Act 2012	Ongoing	N	Use monthly and weekly Assuring Transformation (AT) data to derive performance and quality indicators for Learning Disability services, in order to drive improvements in the services and to identify where good/poor practice is taking place. Analysis will be carried out by NHS England analysts. The analysts will use the fully identifiable data set to produce useful analysis for operational managers and LD Programme staff. The analysis they produce will not include identifiable information. (b) Use timely operational management information, to allow NHS England to monitor and manage delivery of Transforming Care improvements to care for inpatients with a learning disability, behaviour which challenges or an autism spectrum disorder. Unsuppressed small numbers are included in this data set to ensure that commissioners are carrying out their duties in relation to discharging people with a learning disability who are placed inappropriately in hospital. Each CCG is likely to have small numbers in each category and it is important to be able to track if they have reduced their number from e.g. three to two, which unsuppressed numbers do not allow. The operational management report cannot be used for its intended purpose of monitoring commissioner CCG-level activity unless it is populated with unsuppressed data. NHS Digital will be supplying operational management information reports to NHS England on a frequent weekly basis. NHS England will not be doing any processing, but will be using the reports as produced by NHS Digital to manage CCG performance. The new data fields in the AT data set give further insight into delivery of improvements, specifically the Care & Treatment Review process which is used to identify patients suitable for discharge and the barriers currently preventing discharge. Including the new AT fields in the extracts and reports sent to NHS England will facilitate targeted work to discharge patients who have been identified as ready to be discharged from inpatient care. (c) Use information on the location of services and the number of patients using these services to effectively plan and deliver transformational change, reducing the reliance on inpatient care for people with learning disability and/or autism. Planning and delivery will be carried out by Transforming Care Partnerships (TCPs) - CCGs, specialised commissioners and local authorities working together to ensure appropriate and effective services are put in place for this vulnerable group of people. TCPs are responsible for the delivery of the transformation of services, reducing the reliance on inpatient care and using local services to help people live in the community. This will ensure people with learning disability and/or autism receive effective and appropriate care close to their homes. To be able to plan and deliver these new services, TCPs need to have reliable detailed data about the people currently in hospital who originate from their CCGs / local authorities. This will allow them to put appropriate services in place for when patients leave hospital, and to ensure the appropriate provider capacity is available for those people that do still require hospital care. No TCP would see another TCP's unsuppressed data.	Data will not be stored, processed or in any other way accessible by a third party. Data is stored in the secure storage that was set up when NHS England managed this data collection themselves. (a) Monthly data will be analysed to produce aggregate level reports, to allow operational managers to work with challenged organisations to improve delivery and performance against key national indicators. Patient level data will be accessible only to those named individuals that have been given access to the secure data storage, and will only be accessed in the safe haven environment set up for this purpose. (b) NHS Digital supplies operational management information reports to NHS England on a weekly basis. NHS England does not carry out any processing, but ensures the operational MI reports are provided to the named operational managers, who use the reports generated as produced by NHS Digital to manage CCG performance. (c) NHS England will supply data to TCPs to allow them to plan and deliver transformational change to services for people with learning disabilities and/or autism. To allow them to effectively plan and deliver these services they need access to unsuppressed data showing the number of patients originating from the TCP at each hospital site. The data supplied to each TCP will only include information for patients originating from that TCP, and will not include NHS number, date of birth or home postcode.	(a) Outputs are aggregated commissioner-level analysis, used for internal management purposes. The monthly data and analysis allow local operational managers to ensure commissioners are delivering national performance indicators, and to intervene when they are not. (b) The operational MI outputs will only be available to operational managers within NHS England. No other organisations will have access to this data. This information will not be used or shared outside NHS England. [Note that this is a weekly output - section 9 of this template does not include 'weekly' as an option in the Frequency table] Analysis will not be published in journals or be used in relation to clinical trials, nor used for direct marketing. Performance dashboards and other analysis will be used internally and with commissioners once the Management Information has been published by NHS Digital. These will be in aggregated form only. NHS Digitals’ guidance on suppression of small numbers will be followed. (c) Aggregate, unsuppressed TCP-level reports showing the numbers of patients at each provider site, the number of patients at each level of ward security and the numbers of patients in hospital split by length-of-stay groups. This will be used to plan services and identify services which will be decommissioned as services are transformed and bed numbers are reduced.	The data gives insight at organisational (provider/commissioner) level, the benefits are that operational managers will have an evidence base through which to drive improvements in services and patient experience. As soon as this evidence base is available actions can be taken to begin these improvements. Without this evidence base targeted work to improve services and patient experience cannot take place. The data enables performance management of trajectories to reduce inpatient numbers. The information will be used day-to-day, to reduce the reliance on inpatient care and to manage the safe discharge of current inpatients to the community. Benefits will flow immediately as NHS England national and regional managers are able to take immediate action when necessary. Commissioners are developing trajectories for inpatient numbers to March 2017 and these reports will help manage delivery of these trajectories. As well as an in-year delivery target commissioners are developing three-year (2016/17 - 2018/19) transformation plans in line with the published national transformation plan Building the Right Support. Operational management data is important for helping manage delivery of these plans, to ensure any deviation from trajectory is identified early and can be acted on. Detailed information is required by TCPs to plan and commission effectively. The additional request to share detailed data with TCPs will allow TCPs to effectively plan and commission appropriate services, and to reduce the reliance on inpatient care. It will enable patients to be moved from inappropriate inpatient facilities to community care which is closer to home and more appropriate to each individual's needs, improving their quality of life. More effective commissioning of any required inpatient services will save the NHS money, reducing the need for spot-purchasing of care and lengthy block contracts with providers. The data already received by NHS England has allowed them to carry out detailed analysis to support delivery of the Transforming Care programme, in particular the objective of reducing the reliance on inpatient care. NHS England has been able to monitor patients at commissioner level, and identify blockages which are preventing patients being discharged. NHS England have been able to carry out specific pieces of analysis, such as detailed work on the u-18 age group – this has contributed to the reduction in the number of u-18 patients. Changes to the contents of the data set in 2015 included the ‘CCG of origin’ field which has enabled NHS England to map patients whose care is specialised-commissioned by NHS England (over half the inpatient total) to be mapped to their home CCG. This is vital to the process of planning services – without this information local Transforming Care Partnerships (TCPs) do not understand the total number of inpatients that they need to be planning services for. Using this information TCPs have been able to develop 3-year transformation plans. Amending the agreement to allow NHS England to share unsuppressed data with TCPs will allow TCPs to properly and accurately complete these plans, giving them a full understanding of their inpatient numbers and the services these patients are using.
NHS NATIONAL SERVICES SCOTLAND	NHS NATIONAL SERVICES SCOTLAND	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Services Scotland (NSS) Discovery service is an N3 / SWAN based information system providing statistical and comparative benchmarking data updated on a monthly cycle to help Scottish NHS Territorial health boards review, monitor and plan services to support and meet their quality improvement ambitions. The system provides historical data up to four years back and up until the end of the previous calendar month to enable trend analysis. It shows variation between locations including potentially unwarranted variation for a range of indicators. Comparisons are shown for Scottish Health Boards and locations within those, with an agreed set of peers defined to allow effective peer comparisons and benchmarking. Some NHS Boards in Scotland have no natural NHS peer comparisons from within Scotland and so require peers from outwith Scotland. These include, for example, the two largest boards (NHS Greater Glasgow and Clyde and NHS Lothian) and a large national elective centre (the Golden Jubilee hospital). The pseudonymised English data provides the NHS Boards with appropriate peer comparisons. Similar arrangements are in place with NWIS in Wales to provide rural comparisons for the three small island Boards that have no natural peers in Scotland. Providing the NSS Information Services Division with this information allows the researchers to better identify potential areas for improvement, and to understand where best practice is happening. It improves the ability to work with Boards to identify service improvements, leading to better outcomes for patients as well as the chance to consider new sustainable models for productive opportunities and efficiency savings.	The Discovery tool includes data from various sources including Scottish NHS Boards and Health & Social Care, and also inpatient, outpatient, A&E and maternity data from England and Wales. Data is transferred using various secure file transfer methods including the (English) SEFT service. The pseudonymised HES data is provided in a flat-file format. The files are uploaded onto the secure server at NNS and only records from hospitals that are current peers for the relevant hospitals and fields that are required to calculate the current set of performance indicators are transferred to the Discovery data mart. The original files are retained and are re-processed whenever the peer selection for a hospital changes (which, it is anticipated could be once or twice a year at most, depending on evolving customer requirements). The data in the data mart is then aggregated to location and specialty levels, and rates are calculated, and the resulting data extracts drive the visualisations in Tableau. Small numbers are suppressed in line with the HES Analysis guide. Receiving data on all English NHS hospitals allows NNS to select the most appropriate peers for the hospitals based on the latest data, and to be flexible when service changes take place. The list of benchmarking indicators available in NSS Discovery is agreed nationally and is under continuous development (as guided by national governance groups), and, therefore, it is anticipated the number of fields needed to calculate the indicators to gradually increase as well. NSS Discovery is an NHS Scotland Management System built using Tableau software. It can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. The Discovery tool is complemented with a ‘wrap-around service’ (WAS) that provides support to Discovery subscribers including bespoke analysis and reports. The WAS outputs may include figures from English and Welsh peers as defined by the Boards themselves to enhance their benchmarking; for the same or similar indicators, and at the same granularity (small numbers suppressed in line with the HES Analysis Guide) as available in the Discovery tool. The network data storage devices, including Tableau hardware, are located within a secure off-site location at ATOS in Livingston, which is managed under an existing NHS Scotland-wide service contract. Maintenance of the hardware is handled by NSS staff. ATOS will be responsible for the building, location and server room security. All access to the service from the Internet will be via Atos Origin Alliance (AOA) Internet security gateway, a service that provides reverse proxy and Intrusion Prevention System (IPS) functionality. The HES data extracts are shown alongside the Scottish Morbidity Record (SMR) data and the Welsh data, trusts and locations and are shown within peer groups to be used by the NHS Scotland Boards in benchmarking comparisons. Trend analysis across all time points (April 2013 onwards) is available. NSS Discovery is an NHS Scotland Management System which can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. Discovery benchmarking Indicators (outputs) are agreed nationally and are updated to agreed guidelines via appropriate national governance groups. No data from Discovery will be publicly available and data contained within it will not be used for sales and marketing purposes. Only substantive employees of National Services Scotland can access the record-level data. Third parties will only have access to data that is aggregated (with small numbers suppressed in line with the HES Analysis Guide).	Continued inclusion (the HES data being requested as part of this application is already in use with NSS Discovery - Benchmarking Visualisation service) as peer comparators for indicators used for benchmarking and monitoring by NHS Scotland health boards. HES data is currently, and will continue to be, included in interactive tableau visualisations, tables and charts, that are updated on a monthly basis with the latest available data. The data is shown at aggregate level, with small numbers suppressed in line with the HES Analyses Guide, for a number of key indicators that are aligned to the Institute of Medicine’s six dimensions of quality. These outputs are only accessible by approved users within NHS Scotland and the Scottish Government via a secure log in. The Wrap Around Service that supports NSS Discovery provides NHS Boards, when requested, with additional more detailed ad hoc analysis for specific comparators, still at an aggregate level. These ad hoc requests would be produced as management information only in the form of excel tables/charts/presentations etc. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Discovery has a broad user base which extends across the following staff groups within NHS Scotland, Managers, Planners, Analysts, Clinicians, Accountants and Improvement Advisors. They will use the comparative and benchmarking information to underpin service planning and delivery. Discovery provides an Atlas of variation for NHS Boards to quickly assess and understand where there are opportunities to identify and realise improvement prospects e.g.: NHS Boards can use NSS Discovery to identify and learn from better performing peers about how they run their service and to consider if adopting these approaches could improve patient outcomes, drive efficiencies and reduce harm. The Discovery Service has worked with service users to identify these opportunities in 2016-17 and are assisting the boards to make transformational change within their organisations. For some NHS Boards benchmarking information from Scottish peers is insufficient and they particularly value the ability to compare with English peers. This allows them to make more appropriate comparisons, which will support better decision making.
NHS NATIONAL SERVICES SCOTLAND	NHS NATIONAL SERVICES SCOTLAND	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Services Scotland (NSS) Discovery service is an N3 / SWAN based information system providing statistical and comparative benchmarking data updated on a monthly cycle to help Scottish NHS Territorial health boards review, monitor and plan services to support and meet their quality improvement ambitions. The system provides historical data up to four years back and up until the end of the previous calendar month to enable trend analysis. It shows variation between locations including potentially unwarranted variation for a range of indicators. Comparisons are shown for Scottish Health Boards and locations within those, with an agreed set of peers defined to allow effective peer comparisons and benchmarking. Some NHS Boards in Scotland have no natural NHS peer comparisons from within Scotland and so require peers from outwith Scotland. These include, for example, the two largest boards (NHS Greater Glasgow and Clyde and NHS Lothian) and a large national elective centre (the Golden Jubilee hospital). The pseudonymised English data provides the NHS Boards with appropriate peer comparisons. Similar arrangements are in place with NWIS in Wales to provide rural comparisons for the three small island Boards that have no natural peers in Scotland. Providing the NSS Information Services Division with this information allows the researchers to better identify potential areas for improvement, and to understand where best practice is happening. It improves the ability to work with Boards to identify service improvements, leading to better outcomes for patients as well as the chance to consider new sustainable models for productive opportunities and efficiency savings.	The Discovery tool includes data from various sources including Scottish NHS Boards and Health & Social Care, and also inpatient, outpatient, A&E and maternity data from England and Wales. Data is transferred using various secure file transfer methods including the (English) SEFT service. The pseudonymised HES data is provided in a flat-file format. The files are uploaded onto the secure server at NNS and only records from hospitals that are current peers for the relevant hospitals and fields that are required to calculate the current set of performance indicators are transferred to the Discovery data mart. The original files are retained and are re-processed whenever the peer selection for a hospital changes (which, it is anticipated could be once or twice a year at most, depending on evolving customer requirements). The data in the data mart is then aggregated to location and specialty levels, and rates are calculated, and the resulting data extracts drive the visualisations in Tableau. Small numbers are suppressed in line with the HES Analysis guide. Receiving data on all English NHS hospitals allows NNS to select the most appropriate peers for the hospitals based on the latest data, and to be flexible when service changes take place. The list of benchmarking indicators available in NSS Discovery is agreed nationally and is under continuous development (as guided by national governance groups), and, therefore, it is anticipated the number of fields needed to calculate the indicators to gradually increase as well. NSS Discovery is an NHS Scotland Management System built using Tableau software. It can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. The Discovery tool is complemented with a ‘wrap-around service’ (WAS) that provides support to Discovery subscribers including bespoke analysis and reports. The WAS outputs may include figures from English and Welsh peers as defined by the Boards themselves to enhance their benchmarking; for the same or similar indicators, and at the same granularity (small numbers suppressed in line with the HES Analysis Guide) as available in the Discovery tool. The network data storage devices, including Tableau hardware, are located within a secure off-site location at ATOS in Livingston, which is managed under an existing NHS Scotland-wide service contract. Maintenance of the hardware is handled by NSS staff. ATOS will be responsible for the building, location and server room security. All access to the service from the Internet will be via Atos Origin Alliance (AOA) Internet security gateway, a service that provides reverse proxy and Intrusion Prevention System (IPS) functionality. The HES data extracts are shown alongside the Scottish Morbidity Record (SMR) data and the Welsh data, trusts and locations and are shown within peer groups to be used by the NHS Scotland Boards in benchmarking comparisons. Trend analysis across all time points (April 2013 onwards) is available. NSS Discovery is an NHS Scotland Management System which can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. Discovery benchmarking Indicators (outputs) are agreed nationally and are updated to agreed guidelines via appropriate national governance groups. No data from Discovery will be publicly available and data contained within it will not be used for sales and marketing purposes. Only substantive employees of National Services Scotland can access the record-level data. Third parties will only have access to data that is aggregated (with small numbers suppressed in line with the HES Analysis Guide).	Continued inclusion (the HES data being requested as part of this application is already in use with NSS Discovery - Benchmarking Visualisation service) as peer comparators for indicators used for benchmarking and monitoring by NHS Scotland health boards. HES data is currently, and will continue to be, included in interactive tableau visualisations, tables and charts, that are updated on a monthly basis with the latest available data. The data is shown at aggregate level, with small numbers suppressed in line with the HES Analyses Guide, for a number of key indicators that are aligned to the Institute of Medicine’s six dimensions of quality. These outputs are only accessible by approved users within NHS Scotland and the Scottish Government via a secure log in. The Wrap Around Service that supports NSS Discovery provides NHS Boards, when requested, with additional more detailed ad hoc analysis for specific comparators, still at an aggregate level. These ad hoc requests would be produced as management information only in the form of excel tables/charts/presentations etc. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Discovery has a broad user base which extends across the following staff groups within NHS Scotland, Managers, Planners, Analysts, Clinicians, Accountants and Improvement Advisors. They will use the comparative and benchmarking information to underpin service planning and delivery. Discovery provides an Atlas of variation for NHS Boards to quickly assess and understand where there are opportunities to identify and realise improvement prospects e.g.: NHS Boards can use NSS Discovery to identify and learn from better performing peers about how they run their service and to consider if adopting these approaches could improve patient outcomes, drive efficiencies and reduce harm. The Discovery Service has worked with service users to identify these opportunities in 2016-17 and are assisting the boards to make transformational change within their organisations. For some NHS Boards benchmarking information from Scottish peers is insufficient and they particularly value the ability to compare with English peers. This allows them to make more appropriate comparisons, which will support better decision making.
NHS NATIONAL SERVICES SCOTLAND	NHS NATIONAL SERVICES SCOTLAND	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The National Services Scotland (NSS) Discovery service is an N3 / SWAN based information system providing statistical and comparative benchmarking data updated on a monthly cycle to help Scottish NHS Territorial health boards review, monitor and plan services to support and meet their quality improvement ambitions. The system provides historical data up to four years back and up until the end of the previous calendar month to enable trend analysis. It shows variation between locations including potentially unwarranted variation for a range of indicators. Comparisons are shown for Scottish Health Boards and locations within those, with an agreed set of peers defined to allow effective peer comparisons and benchmarking. Some NHS Boards in Scotland have no natural NHS peer comparisons from within Scotland and so require peers from outwith Scotland. These include, for example, the two largest boards (NHS Greater Glasgow and Clyde and NHS Lothian) and a large national elective centre (the Golden Jubilee hospital). The pseudonymised English data provides the NHS Boards with appropriate peer comparisons. Similar arrangements are in place with NWIS in Wales to provide rural comparisons for the three small island Boards that have no natural peers in Scotland. Providing the NSS Information Services Division with this information allows the researchers to better identify potential areas for improvement, and to understand where best practice is happening. It improves the ability to work with Boards to identify service improvements, leading to better outcomes for patients as well as the chance to consider new sustainable models for productive opportunities and efficiency savings.	The Discovery tool includes data from various sources including Scottish NHS Boards and Health & Social Care, and also inpatient, outpatient, A&E and maternity data from England and Wales. Data is transferred using various secure file transfer methods including the (English) SEFT service. The pseudonymised HES data is provided in a flat-file format. The files are uploaded onto the secure server at NNS and only records from hospitals that are current peers for the relevant hospitals and fields that are required to calculate the current set of performance indicators are transferred to the Discovery data mart. The original files are retained and are re-processed whenever the peer selection for a hospital changes (which, it is anticipated could be once or twice a year at most, depending on evolving customer requirements). The data in the data mart is then aggregated to location and specialty levels, and rates are calculated, and the resulting data extracts drive the visualisations in Tableau. Small numbers are suppressed in line with the HES Analysis guide. Receiving data on all English NHS hospitals allows NNS to select the most appropriate peers for the hospitals based on the latest data, and to be flexible when service changes take place. The list of benchmarking indicators available in NSS Discovery is agreed nationally and is under continuous development (as guided by national governance groups), and, therefore, it is anticipated the number of fields needed to calculate the indicators to gradually increase as well. NSS Discovery is an NHS Scotland Management System built using Tableau software. It can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. The Discovery tool is complemented with a ‘wrap-around service’ (WAS) that provides support to Discovery subscribers including bespoke analysis and reports. The WAS outputs may include figures from English and Welsh peers as defined by the Boards themselves to enhance their benchmarking; for the same or similar indicators, and at the same granularity (small numbers suppressed in line with the HES Analysis Guide) as available in the Discovery tool. The network data storage devices, including Tableau hardware, are located within a secure off-site location at ATOS in Livingston, which is managed under an existing NHS Scotland-wide service contract. Maintenance of the hardware is handled by NSS staff. ATOS will be responsible for the building, location and server room security. All access to the service from the Internet will be via Atos Origin Alliance (AOA) Internet security gateway, a service that provides reverse proxy and Intrusion Prevention System (IPS) functionality. The HES data extracts are shown alongside the Scottish Morbidity Record (SMR) data and the Welsh data, trusts and locations and are shown within peer groups to be used by the NHS Scotland Boards in benchmarking comparisons. Trend analysis across all time points (April 2013 onwards) is available. NSS Discovery is an NHS Scotland Management System which can only be accessed by approved users within NHS Scotland health boards and the Scottish Government via a secure log in. The Discovery service is funded by the NHS Scotland Territorial Health Boards directly to NSS-ISD with money top sliced from the Scottish Government. Discovery benchmarking Indicators (outputs) are agreed nationally and are updated to agreed guidelines via appropriate national governance groups. No data from Discovery will be publicly available and data contained within it will not be used for sales and marketing purposes. Only substantive employees of National Services Scotland can access the record-level data. Third parties will only have access to data that is aggregated (with small numbers suppressed in line with the HES Analysis Guide).	Continued inclusion (the HES data being requested as part of this application is already in use with NSS Discovery - Benchmarking Visualisation service) as peer comparators for indicators used for benchmarking and monitoring by NHS Scotland health boards. HES data is currently, and will continue to be, included in interactive tableau visualisations, tables and charts, that are updated on a monthly basis with the latest available data. The data is shown at aggregate level, with small numbers suppressed in line with the HES Analyses Guide, for a number of key indicators that are aligned to the Institute of Medicine’s six dimensions of quality. These outputs are only accessible by approved users within NHS Scotland and the Scottish Government via a secure log in. The Wrap Around Service that supports NSS Discovery provides NHS Boards, when requested, with additional more detailed ad hoc analysis for specific comparators, still at an aggregate level. These ad hoc requests would be produced as management information only in the form of excel tables/charts/presentations etc. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Discovery has a broad user base which extends across the following staff groups within NHS Scotland, Managers, Planners, Analysts, Clinicians, Accountants and Improvement Advisors. They will use the comparative and benchmarking information to underpin service planning and delivery. Discovery provides an Atlas of variation for NHS Boards to quickly assess and understand where there are opportunities to identify and realise improvement prospects e.g.: NHS Boards can use NSS Discovery to identify and learn from better performing peers about how they run their service and to consider if adopting these approaches could improve patient outcomes, drive efficiencies and reduce harm. The Discovery Service has worked with service users to identify these opportunities in 2016-17 and are assisting the boards to make transformational change within their organisations. For some NHS Boards benchmarking information from Scottish peers is insufficient and they particularly value the ability to compare with English peers. This allows them to make more appropriate comparisons, which will support better decision making.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	NHS NORTH AND EAST LONDON COMMISSIONING SUPPORT UNIT	Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The HES data will only be used by North and East London Commissioning Support Unit (NELCSU) to provide support to Clinical Commissioning Groups (CCGs) and other commissioning bodies working with NELCSU to meet their statutory duties under the Health & Social Care Act 2012 and NHS health economy wide transformation projects that require detailed hospital level data. The pseudonymised record level HES data is interrogated only by approved NELCSU substantively employed analysts to provide benchmarking and comparative information to NELCSU clients and NHS health economy wide transformation projects that require detailed hospital level data. The full, national set of HES data allows complex and detailed modelling and benchmarking of activity, essential to successful commissioning of services and contract monitoring, including analysing relationships and influences between A&E, Inpatient and outpatient care. This will especially support benchmarking work for CCG clients taking part in health economy wide transformation projects that require detailed and comprehensive hospital level data (for example from local SUS data feeds the applicant only receives data for their CCG’s registered population, which does not allow whole trust activity to be considered) and allow CCGs to benchmark and identify best practice in similar health economies anywhere in England. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.	Only an approved list of NELCSU substantively employed analysts have access to the full set of pseudonymised data tables, via secure server based Structured Query Language (SQL) querying. The data will only be for the purposes described in this document and not for any other purposes, including being used in data tools. CSU analysts interrogate the data to produce aggregated output for monitoring care outcome and activity for a CCG’s population, patient group, condition or service provider, including trends over time in any given activity or care process. For example, trends over time can be modelled to produce forecasts of future activity, taking into account population growth or changes in service configuration. National data is necessary to benchmark against any CCG peer groups (as defined by NHS England), or any other care pathway or group of patients. Benchmarking allows an individual CCG to evaluate its own care processes and outcomes against other similar commissioning populations, with a view to identifying areas for improvement or to identify best practice. National data also supports NHS health economy wide transformation projects or other commissioning initiatives that require detailed and comprehensive hospital level data. Analysts will only release aggregated data with small numbers suppressed in line with the HES Analysis Guide. The data is being held within a data centre which also holds data on behalf of other organisations. The applicant agrees that the data under this agreement must be held and remain separate to all other data (except where explicitly stated within the agreement), and accepts full responsibility for the breach of this agreement should this not be the case. In order to mitigate this risk, it is strongly recommended that the applicant considers the best practice controls as detailed in ISO 27017:2015 Code of practice for information security controls based on ISO/IEC 27002 and ISO 27018:2014, which establish commonly accepted control objectives, controls and guidelines for implementing measures to protect Personally Identifiable Data. For clarity, any access by Interxion to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.	Outputs are on an on going basis (i.e., no target date) as the HES data in general is used to support general commissioning and public health needs, and is not aimed at a specific report or deadline for use. All outputs informed by information retrieved from the HES data tables are governed by adherence to the HES guidance on suppression of small numbers. Users of the data abide by the HES Analysis Guide which means that all outputs released must be aggregated with small numbers suppressed. HES allows NELCSU to provide intelligence for programmes whose scope demands activity benchmarking of the CSU's clients (CCGs) against similar health economies or populations in England. SUS data does not allow this scope. National data also supports NHS health economy wide transformation projects that require detailed and comprehensive hospital level data. Commissioners can compare with any service known to have better outcomes or new pathways, or support large scale transformation projects that may impact several commissioners across, for example, the North London area. Outputs expected are aggregated data to support reports or decisions across examples such as the following: • Elements of Joint Strategic Needs Assessments (JSNA) - to support CCGs/Local Authorities to consider the needs of their local populations and in how they respond with effective commissioning of services to properly meet those needs, by enabling, for example views of the use of secondary services by different patient groups by condition, ethnicity, etc. • Quality, Innovation, Productivity and Prevention (QIPP) development - identifying and benchmarking areas across England with better practice than locally, to help evaluate high costs and poor outcomes in hospital care. • Providing data on hospital admissions in-year to support monitoring of national ambitions, such as avoiding unnecessary admissions across CCGs, by practice, condition, hospital trust. CCGs are required to monitor and make progress on national outcome measures and ambitions by NHS England, and use of national benchmarking is promoted heavily by initiatives such as Right Care ‘Commissioning for Value’ (on behalf of NHSE). Without access to national data such as HES, CCGs cannot be ultimately certain that they are making progress or making decisions on the best basis possible. Diagnostic Imaging is an acknowledged area of over/ duplicate treatment and so a fruitful area for NELCSU to investigate and to support improvement initiatives (eg Right Care). The DIDs data with linkage to HES will help with any deep dives and provide further opportunities for gaining insight from this data. As an example some of NELCSU customer CCGs have very high diagnostic intervention rates per head of population (eg for MRIs). Having DIDs data allows NELCSU to have the detailed data to be able to investigate these type of issues in more detail and provide useful outputs.	CCGs and Local Authorities (Public Health teams) have joint statutory duties under the Health and Social Care Act 2012 to plan and commission services and jointly assess the needs of their patients and populations, to ensure that health improvements and better outcomes are measurable, identifiable and attainable. Analysis of HES and DIDs data helps these organisations achieve this by providing the greatest scope to evaluate outcomes of care and improvement in their health services against peer groups and national achievement – providing a more extensive and complete base of knowledge for decision making than data on their own patients alone (SUS data). Measurable benefits can occur, for example, through gradual improvement in outcome over a number of years, to more immediate commissioning new services where a gap is identified, or de-commissioning failing services by identifying lower outcomes than is acceptable, compared to the norm. Examples of benefits achieved to date include: a. NELCSU supported a major reconfiguration of cancer and cardiac services in North London. The detailed numbers to support the case for change were extracted from the raw HES data. This was a complex exercise, requiring clinical input to define the primary and secondary coding of the patient cohorts affected by the change. This would only be possible through using very granular data which covers whole hospital activity (rather than for our customer CCGs). The rigour of the work helped with forming realistic planning assumptions and obtaining clinical and provider buy in for changes. b. NELCSU are currently supporting NHS England with work identifying the highest and lowest referrers within London by CCG and by GP Practices within each CCG. This work requires whole London data and as it adopts age and sex standardisation needs data to a very granular level. We have also applied certain filters that improve the accuracy of the benchmarking from detailed analysis of the data. This project is currently supporting NHS England in a London-wide demand management programme which aims to ease the pressure on acute hospitals by targeting those CCGs and practices with abnormally referral rates.
NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS North of England Commissioning Support (NECS) provides a comprehensive business intelligence (BI) service to a wide range of NHS organisations. This includes both standard analytics and reporting, and deep-dives and diagnostic exercises to offer insight and intelligence on a commissioner’s health economy. In addition NECS offer a mature business intelligence application (RAIDR) allowing self-service access to a range of dashboards and configurable reports. This tool is available on a subscription basis only to NHS organisations and local authorities. It is currently used by Clinical Commissioning Groups (CCGs), internally within the CSU through specialist support teams and by CCG member practices. A list of current customers is attached as a supporting document (SD1). Both the business intelligence team and RAIDR utilise data feeds from secondary and community care, mental health services, urgent and primary care, prescribing and other HSCIC published datasets such as QOF, RTT etc. These data sets are provided either by the DSCRO service, downloaded directly the NHS England data catalogue or directly from provider organisations. Data delivered via the DSCRO is pseudonymised in to the CSU where the BI service and RAIDR are hosted. Published data is downloaded in aggregate form from HSCIC and NHS England websites. There will be no direct linkage between HES data records and other data already used by the CSU and in its BI tool (RAIDR). HES data may be presented alongside other data but not linked to it – for example a report may contain HES data alongside workforce statistics, weather reports etc. Typical uses for the business intelligence service and tool include • Provision of contract, performance and quality monitoring of commissioned services – this ensures CCGs are empowered with intelligence on the services they are accountable for and can undertake their statutory duties • Fully embrace clinical commissioning – CCGs have taken steps to delegate their some of their commissioning responsibility to their member practices. The RAIDR tool is used to present practice level activity and performance information allowing GP practices to assess how their local initiatives affect wider service utilization. For example, does opening their surgery later in the evening reduce the burden on A&E? Can they evidence a change in A&E usage from the point they opened later? • The national drive to progress the Better Care Fund and Vanguard alternate care models both require in-depth analytics. Access to timely information showing the impact of service transformations is key to evidencing the success of these national NHS programmes. • To support the on-going budgetary pressures the NHS is faced the business intelligence service and RAIDR offer significant support to commissioners on their QIPP programmes. Identifying service areas where the commissioner is an outlier that may then require re-procurement of a clinical service, comparisons with peer groups and best practice to understand how a change in approach might deliver a financial saving NECS is requesting HES data that will enable users of their business intelligence service and the RAIDR tool to compare themselves on a national footprint. For benchmarking, national data is needed to allow comparisons due to the number of customer organisations and number of types of organisations. This would be a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. The HES data will be utilized within RAIDR to provide a range of benchmarking dashboards and reports as required to address customer specific priorities. This may include mortality, end of life, procedures of limited clinical value, new to review ratios, readmissions etc. The ability to present a national and peer-group picture of locally defined indicators is the ambition. HES data will be presented independently of existing data flows within a bespoke dashboard as well as to supplement current reports/dashboards. For example using HES to calculate a national readmission rate to be presented on a locally fed readmission report. As well as within the RAIDR tool HES data will be used by the BI team for bespoke analytics and reporting. This will include for individual commissioners who have requested a deep-dive for a particular area and want to understand how they compare to other areas. It will also help support whole provider and health economy analysis where service re-configurations are being proposed. [Note: this is not possible with the commissioner specific slices of data provisioned via the DSCRO]	1. Data will be received and stored by the data management service within NECS. This is a dedicated team responsible for the organisations data warehouses and incoming/outgoing flows of data. The HES datasets will initially land in the teams secure file share before being uploaded in to an SQL Server data warehouse. Both file share and SQL server data are securely hosted within a commercial grade data centre. 2. The data management service will create derived fields based on the data received such as Ambulatory care sensitive condition flag, procedure of limited clinical value flag etc. 3. Data will be used to populate secure data cubes for use by analysts within the CSU. The data being made available will be record level but no identifiable data will be included. Only the minimum required data fields will be used to populate each cube. 4. Data will be used by the RAIDR support team to populate the relevant dashboards and reports within the RAIDR system. No patient level data will be available to RAIDR users. Small number suppression rules will be adhered to. 5. Record level HES data will not be directly linked to any other dataset. Staff follow strict rules on accessing, analysing and processing data. Only aggregate data will leave the CSU. All small numbers will be suppressed before any data is made visible to customers outside of the organisation. Small numbers will be suppressed in line with the HES analysis guide. Pseudonymised, rather than anonymised, data is required to enable calculation of benchmarked metrics on a per patient basis e.g. average number of A&E attendances per patient. For clarity, this request is for non-identifiable, pseudonymised data to flow into the data management team of North of England Commissioning Support Unit.	As described in the objectives the outputs will be two-fold: 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality b. Readmissions c. New to review ratios for outpatients d. Procedures of limited clinical value e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions. Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. 2) In addition to self-service dashboards (see above) the applicant will utilize the HES datasets to undertake various analyses both locally and in support of a range of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data would add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. The business intelligence teams within NECS would use the HES data to produce deep-dive reports and analysis on specific projects whilst ensuring small number suppression is followed for all outputs and no data is shared outside the organisation. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level. Future outputs over the next 12 months (some of which is work in progress) Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. In addition to self-service dashboards NECS will continue to utilise the HES datasets to undertake various analyses both locally and in support of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data will add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level.	Utilising HES data for reporting will provide more accurate peer groups for benchmarking purposes rather than simply comparing neighboring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. For example NECS recently delivered a project for NHS England to help improve the treatment of patients with dementia. National figures suggested the dementia registers in GP practices had patients missing. A dementia dashboard was developed within RAIDR to highlight to GP practices and commissioners where practices potentially had patients missing from their register based on secondary care SUS data – this was successful with the number of patients on dementia registers increasing however it could only be undertaken locally as NECS did not hold a national dataset. Another example is where the CSU (on behalf of a local CCG) has undertaken reporting of emergency admission rates with a view to altering patient pathways. Emergency admission rates for local providers have been used to identify best practice and pathways altered to reduce admissions. However this exercise was limited to local hospitals as NECS did not have national data available. The CCG were then able to compare their local pathways with others where readmission rates were lower with a view to changing how services were configured and commissioned locally. Ideally this would have allowed the CCG to compare their readmission rates with all commissioners/providers nationally but this was not possible without access to the full HES dataset. When the CSU come to extend/renew their agreement, evidence will be supplied for benefits achieved through the provision of dashboards/analysis to each type of customer organisation. What has been achieved - Considerable benchmarking work across the North East to look for QIPP savings, and the ability to drill down into the datasets provided by HES to understand variation at low levels of granularity. However, even if we managed to bring every CCG down to the level of the best in the North East we’d still be outliers nationally, as the North East is a national outlier. To this end, HES allows us to place the North East into context with other sub-regions from across the country, using identical queries, to help to explain some of the limitations of QIPP schemes, but also some of the true opportunities. Utilising HES data for reporting provides more accurate peer groups for benchmarking purposes rather than simply comparing neighbouring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. This is a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. NECS have identified areas of potential savings as in RightCare focus packs, and been able to validate, benchmark and investigate further. E.g. if a CCG has been identified as an outlier for spend on a certain procedure, are activity levels also high or is it just the procedure that is expensive? How do they compare nationally and against their 10 similar CCGs? Is the provider itself an outlier? And are there other similar procedures where underperformance cancels out the potential savings. This gives the CCG confidence in discussions with providers, as more often than not the figures in the RightCare focus packs are questioned by the trusts. A RAIDR dashboard has been developed which supports the Rightcare approach this is currently in testing phase. Using HES NECS have done national comparisons against peer groups to the levels that Right Care don’t do, do more deep dive reporting into the key areas of overspend, provide comparative analysis in our routine reporting against peer CCGs, and generally make use of best national information resource. It has facilitated our CCGs to make contact with “best in class” CCGs, identified through the interrogation of HES, who have, for example, the lowest activity rates in some of the areas we’ve been looking at. This has resulted in useful discussions with those CCGs about their activity levels. Deep-dives have been completed for individual commissioners who want to understand how they compare to other areas to help support whole provider and health economy analysis where service reconfigurations are being proposed. NECS have utilised HES data to inform baseline capacity and demand positions for the acute sector in STPs in our footprint. In particular, one of our STPs which is well progressed, is utilising HES data to inform a capacity planning model to forecast future demand and produce scenarios for future hospital configuration. This STP includes a CCG that is out with our traditional CCG footprint, therefore the HES data provides a consistent, baseline from which this modelling can be carried out. NECS are carrying out a national project to test the effect of a range of factors on patient’s decision to attend A&E departments. In order to derive the independent variable for this model, HES was used to calculate A&E Attendance rates for each of the 5000+ GP Practices within the study. In order to provide context for the North East and Cumbria’s Urgent and Emergency Care 5 Year Strategy, HES has been used to calculate A&E Attendance, Emergency Admission and Emergency Bed Day rates for all CCGs in England in order to produce funnel plots to highlight which CCGs within the network footprint are statistical outliers and provide evidence for action within the strategy. Future benefits over the next 12 months (some of which is work in progress) 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality. NECS were previously reliant on third party commercial products in order to produce mortality reports for our local CCGs. The availability of HES is allowing us to calculate our own mortality indices (for example HSMR) and this is currently being incorporated within our Quality & Performance dashboard within RAIDR. In addition the functionality of RAIDR is such that further deep dives, the ability to compare with Trust peer groups nationally and modifying parameters within the mortality calculations (e.g. looking at 7 day, 30 day, 60 day timeframes) will provide additional intelligence to our health system on hospital mortality. b. Readmissions. Although nationally published statistics are published periodically on readmission rates, these are often at a relatively high level of aggregation, often with a significant time-lag and working to a definition that we don’t believe is necessarily the most appropriate for the types of insight that we are attempting to provide. Within NECS we have the ability for our commissioners to drill down to more appropriate levels of granularity, in a more timely fashion and to our own, locally developed and agreed definition. However, we can provide better comparative analysis and therefore more insight by running similar queries for commissioners within our CCG peer groups, but outwith the North East. Furthermore we are currently producing similar analyses for neighbouring CCGs who do not have access to the skills or underlying data to produce this. c. New to review ratios for outpatients d. Procedures of limited clinical value Within RAIDR NECS currently report on the volume of activity commissioned by each of our CCGs which a part of the Procedures of Limited Clinical Value policy, which is updated each year. There are of course exclusions applied to each of these and using a combination of procedure and diagnosis codes this reports provides intelligence on the number of procedures that are still performed. Being able to apply our algorithm to other commissioners outside of our area will allow NECS to identify where perhaps other commissioners are applying similar rules more stringently, so that NECS can learn from them, and also where other areas are not restricting particular procedures which might lead us to change our policy. e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions.
NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS North of England Commissioning Support (NECS) provides a comprehensive business intelligence (BI) service to a wide range of NHS organisations. This includes both standard analytics and reporting, and deep-dives and diagnostic exercises to offer insight and intelligence on a commissioner’s health economy. In addition NECS offer a mature business intelligence application (RAIDR) allowing self-service access to a range of dashboards and configurable reports. This tool is available on a subscription basis only to NHS organisations and local authorities. It is currently used by Clinical Commissioning Groups (CCGs), internally within the CSU through specialist support teams and by CCG member practices. A list of current customers is attached as a supporting document (SD1). Both the business intelligence team and RAIDR utilise data feeds from secondary and community care, mental health services, urgent and primary care, prescribing and other HSCIC published datasets such as QOF, RTT etc. These data sets are provided either by the DSCRO service, downloaded directly the NHS England data catalogue or directly from provider organisations. Data delivered via the DSCRO is pseudonymised in to the CSU where the BI service and RAIDR are hosted. Published data is downloaded in aggregate form from HSCIC and NHS England websites. There will be no direct linkage between HES data records and other data already used by the CSU and in its BI tool (RAIDR). HES data may be presented alongside other data but not linked to it – for example a report may contain HES data alongside workforce statistics, weather reports etc. Typical uses for the business intelligence service and tool include • Provision of contract, performance and quality monitoring of commissioned services – this ensures CCGs are empowered with intelligence on the services they are accountable for and can undertake their statutory duties • Fully embrace clinical commissioning – CCGs have taken steps to delegate their some of their commissioning responsibility to their member practices. The RAIDR tool is used to present practice level activity and performance information allowing GP practices to assess how their local initiatives affect wider service utilization. For example, does opening their surgery later in the evening reduce the burden on A&E? Can they evidence a change in A&E usage from the point they opened later? • The national drive to progress the Better Care Fund and Vanguard alternate care models both require in-depth analytics. Access to timely information showing the impact of service transformations is key to evidencing the success of these national NHS programmes. • To support the on-going budgetary pressures the NHS is faced the business intelligence service and RAIDR offer significant support to commissioners on their QIPP programmes. Identifying service areas where the commissioner is an outlier that may then require re-procurement of a clinical service, comparisons with peer groups and best practice to understand how a change in approach might deliver a financial saving NECS is requesting HES data that will enable users of their business intelligence service and the RAIDR tool to compare themselves on a national footprint. For benchmarking, national data is needed to allow comparisons due to the number of customer organisations and number of types of organisations. This would be a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. The HES data will be utilized within RAIDR to provide a range of benchmarking dashboards and reports as required to address customer specific priorities. This may include mortality, end of life, procedures of limited clinical value, new to review ratios, readmissions etc. The ability to present a national and peer-group picture of locally defined indicators is the ambition. HES data will be presented independently of existing data flows within a bespoke dashboard as well as to supplement current reports/dashboards. For example using HES to calculate a national readmission rate to be presented on a locally fed readmission report. As well as within the RAIDR tool HES data will be used by the BI team for bespoke analytics and reporting. This will include for individual commissioners who have requested a deep-dive for a particular area and want to understand how they compare to other areas. It will also help support whole provider and health economy analysis where service re-configurations are being proposed. [Note: this is not possible with the commissioner specific slices of data provisioned via the DSCRO]	1. Data will be received and stored by the data management service within NECS. This is a dedicated team responsible for the organisations data warehouses and incoming/outgoing flows of data. The HES datasets will initially land in the teams secure file share before being uploaded in to an SQL Server data warehouse. Both file share and SQL server data are securely hosted within a commercial grade data centre. 2. The data management service will create derived fields based on the data received such as Ambulatory care sensitive condition flag, procedure of limited clinical value flag etc. 3. Data will be used to populate secure data cubes for use by analysts within the CSU. The data being made available will be record level but no identifiable data will be included. Only the minimum required data fields will be used to populate each cube. 4. Data will be used by the RAIDR support team to populate the relevant dashboards and reports within the RAIDR system. No patient level data will be available to RAIDR users. Small number suppression rules will be adhered to. 5. Record level HES data will not be directly linked to any other dataset. Staff follow strict rules on accessing, analysing and processing data. Only aggregate data will leave the CSU. All small numbers will be suppressed before any data is made visible to customers outside of the organisation. Small numbers will be suppressed in line with the HES analysis guide. Pseudonymised, rather than anonymised, data is required to enable calculation of benchmarked metrics on a per patient basis e.g. average number of A&E attendances per patient. For clarity, this request is for non-identifiable, pseudonymised data to flow into the data management team of North of England Commissioning Support Unit.	As described in the objectives the outputs will be two-fold: 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality b. Readmissions c. New to review ratios for outpatients d. Procedures of limited clinical value e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions. Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. 2) In addition to self-service dashboards (see above) the applicant will utilize the HES datasets to undertake various analyses both locally and in support of a range of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data would add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. The business intelligence teams within NECS would use the HES data to produce deep-dive reports and analysis on specific projects whilst ensuring small number suppression is followed for all outputs and no data is shared outside the organisation. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level. Future outputs over the next 12 months (some of which is work in progress) Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. In addition to self-service dashboards NECS will continue to utilise the HES datasets to undertake various analyses both locally and in support of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data will add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level.	Utilising HES data for reporting will provide more accurate peer groups for benchmarking purposes rather than simply comparing neighboring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. For example NECS recently delivered a project for NHS England to help improve the treatment of patients with dementia. National figures suggested the dementia registers in GP practices had patients missing. A dementia dashboard was developed within RAIDR to highlight to GP practices and commissioners where practices potentially had patients missing from their register based on secondary care SUS data – this was successful with the number of patients on dementia registers increasing however it could only be undertaken locally as NECS did not hold a national dataset. Another example is where the CSU (on behalf of a local CCG) has undertaken reporting of emergency admission rates with a view to altering patient pathways. Emergency admission rates for local providers have been used to identify best practice and pathways altered to reduce admissions. However this exercise was limited to local hospitals as NECS did not have national data available. The CCG were then able to compare their local pathways with others where readmission rates were lower with a view to changing how services were configured and commissioned locally. Ideally this would have allowed the CCG to compare their readmission rates with all commissioners/providers nationally but this was not possible without access to the full HES dataset. When the CSU come to extend/renew their agreement, evidence will be supplied for benefits achieved through the provision of dashboards/analysis to each type of customer organisation. What has been achieved - Considerable benchmarking work across the North East to look for QIPP savings, and the ability to drill down into the datasets provided by HES to understand variation at low levels of granularity. However, even if we managed to bring every CCG down to the level of the best in the North East we’d still be outliers nationally, as the North East is a national outlier. To this end, HES allows us to place the North East into context with other sub-regions from across the country, using identical queries, to help to explain some of the limitations of QIPP schemes, but also some of the true opportunities. Utilising HES data for reporting provides more accurate peer groups for benchmarking purposes rather than simply comparing neighbouring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. This is a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. NECS have identified areas of potential savings as in RightCare focus packs, and been able to validate, benchmark and investigate further. E.g. if a CCG has been identified as an outlier for spend on a certain procedure, are activity levels also high or is it just the procedure that is expensive? How do they compare nationally and against their 10 similar CCGs? Is the provider itself an outlier? And are there other similar procedures where underperformance cancels out the potential savings. This gives the CCG confidence in discussions with providers, as more often than not the figures in the RightCare focus packs are questioned by the trusts. A RAIDR dashboard has been developed which supports the Rightcare approach this is currently in testing phase. Using HES NECS have done national comparisons against peer groups to the levels that Right Care don’t do, do more deep dive reporting into the key areas of overspend, provide comparative analysis in our routine reporting against peer CCGs, and generally make use of best national information resource. It has facilitated our CCGs to make contact with “best in class” CCGs, identified through the interrogation of HES, who have, for example, the lowest activity rates in some of the areas we’ve been looking at. This has resulted in useful discussions with those CCGs about their activity levels. Deep-dives have been completed for individual commissioners who want to understand how they compare to other areas to help support whole provider and health economy analysis where service reconfigurations are being proposed. NECS have utilised HES data to inform baseline capacity and demand positions for the acute sector in STPs in our footprint. In particular, one of our STPs which is well progressed, is utilising HES data to inform a capacity planning model to forecast future demand and produce scenarios for future hospital configuration. This STP includes a CCG that is out with our traditional CCG footprint, therefore the HES data provides a consistent, baseline from which this modelling can be carried out. NECS are carrying out a national project to test the effect of a range of factors on patient’s decision to attend A&E departments. In order to derive the independent variable for this model, HES was used to calculate A&E Attendance rates for each of the 5000+ GP Practices within the study. In order to provide context for the North East and Cumbria’s Urgent and Emergency Care 5 Year Strategy, HES has been used to calculate A&E Attendance, Emergency Admission and Emergency Bed Day rates for all CCGs in England in order to produce funnel plots to highlight which CCGs within the network footprint are statistical outliers and provide evidence for action within the strategy. Future benefits over the next 12 months (some of which is work in progress) 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality. NECS were previously reliant on third party commercial products in order to produce mortality reports for our local CCGs. The availability of HES is allowing us to calculate our own mortality indices (for example HSMR) and this is currently being incorporated within our Quality & Performance dashboard within RAIDR. In addition the functionality of RAIDR is such that further deep dives, the ability to compare with Trust peer groups nationally and modifying parameters within the mortality calculations (e.g. looking at 7 day, 30 day, 60 day timeframes) will provide additional intelligence to our health system on hospital mortality. b. Readmissions. Although nationally published statistics are published periodically on readmission rates, these are often at a relatively high level of aggregation, often with a significant time-lag and working to a definition that we don’t believe is necessarily the most appropriate for the types of insight that we are attempting to provide. Within NECS we have the ability for our commissioners to drill down to more appropriate levels of granularity, in a more timely fashion and to our own, locally developed and agreed definition. However, we can provide better comparative analysis and therefore more insight by running similar queries for commissioners within our CCG peer groups, but outwith the North East. Furthermore we are currently producing similar analyses for neighbouring CCGs who do not have access to the skills or underlying data to produce this. c. New to review ratios for outpatients d. Procedures of limited clinical value Within RAIDR NECS currently report on the volume of activity commissioned by each of our CCGs which a part of the Procedures of Limited Clinical Value policy, which is updated each year. There are of course exclusions applied to each of these and using a combination of procedure and diagnosis codes this reports provides intelligence on the number of procedures that are still performed. Being able to apply our algorithm to other commissioners outside of our area will allow NECS to identify where perhaps other commissioners are applying similar rules more stringently, so that NECS can learn from them, and also where other areas are not restricting particular procedures which might lead us to change our policy. e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions.
NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS North of England Commissioning Support (NECS) provides a comprehensive business intelligence (BI) service to a wide range of NHS organisations. This includes both standard analytics and reporting, and deep-dives and diagnostic exercises to offer insight and intelligence on a commissioner’s health economy. In addition NECS offer a mature business intelligence application (RAIDR) allowing self-service access to a range of dashboards and configurable reports. This tool is available on a subscription basis only to NHS organisations and local authorities. It is currently used by Clinical Commissioning Groups (CCGs), internally within the CSU through specialist support teams and by CCG member practices. A list of current customers is attached as a supporting document (SD1). Both the business intelligence team and RAIDR utilise data feeds from secondary and community care, mental health services, urgent and primary care, prescribing and other HSCIC published datasets such as QOF, RTT etc. These data sets are provided either by the DSCRO service, downloaded directly the NHS England data catalogue or directly from provider organisations. Data delivered via the DSCRO is pseudonymised in to the CSU where the BI service and RAIDR are hosted. Published data is downloaded in aggregate form from HSCIC and NHS England websites. There will be no direct linkage between HES data records and other data already used by the CSU and in its BI tool (RAIDR). HES data may be presented alongside other data but not linked to it – for example a report may contain HES data alongside workforce statistics, weather reports etc. Typical uses for the business intelligence service and tool include • Provision of contract, performance and quality monitoring of commissioned services – this ensures CCGs are empowered with intelligence on the services they are accountable for and can undertake their statutory duties • Fully embrace clinical commissioning – CCGs have taken steps to delegate their some of their commissioning responsibility to their member practices. The RAIDR tool is used to present practice level activity and performance information allowing GP practices to assess how their local initiatives affect wider service utilization. For example, does opening their surgery later in the evening reduce the burden on A&E? Can they evidence a change in A&E usage from the point they opened later? • The national drive to progress the Better Care Fund and Vanguard alternate care models both require in-depth analytics. Access to timely information showing the impact of service transformations is key to evidencing the success of these national NHS programmes. • To support the on-going budgetary pressures the NHS is faced the business intelligence service and RAIDR offer significant support to commissioners on their QIPP programmes. Identifying service areas where the commissioner is an outlier that may then require re-procurement of a clinical service, comparisons with peer groups and best practice to understand how a change in approach might deliver a financial saving NECS is requesting HES data that will enable users of their business intelligence service and the RAIDR tool to compare themselves on a national footprint. For benchmarking, national data is needed to allow comparisons due to the number of customer organisations and number of types of organisations. This would be a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. The HES data will be utilized within RAIDR to provide a range of benchmarking dashboards and reports as required to address customer specific priorities. This may include mortality, end of life, procedures of limited clinical value, new to review ratios, readmissions etc. The ability to present a national and peer-group picture of locally defined indicators is the ambition. HES data will be presented independently of existing data flows within a bespoke dashboard as well as to supplement current reports/dashboards. For example using HES to calculate a national readmission rate to be presented on a locally fed readmission report. As well as within the RAIDR tool HES data will be used by the BI team for bespoke analytics and reporting. This will include for individual commissioners who have requested a deep-dive for a particular area and want to understand how they compare to other areas. It will also help support whole provider and health economy analysis where service re-configurations are being proposed. [Note: this is not possible with the commissioner specific slices of data provisioned via the DSCRO]	1. Data will be received and stored by the data management service within NECS. This is a dedicated team responsible for the organisations data warehouses and incoming/outgoing flows of data. The HES datasets will initially land in the teams secure file share before being uploaded in to an SQL Server data warehouse. Both file share and SQL server data are securely hosted within a commercial grade data centre. 2. The data management service will create derived fields based on the data received such as Ambulatory care sensitive condition flag, procedure of limited clinical value flag etc. 3. Data will be used to populate secure data cubes for use by analysts within the CSU. The data being made available will be record level but no identifiable data will be included. Only the minimum required data fields will be used to populate each cube. 4. Data will be used by the RAIDR support team to populate the relevant dashboards and reports within the RAIDR system. No patient level data will be available to RAIDR users. Small number suppression rules will be adhered to. 5. Record level HES data will not be directly linked to any other dataset. Staff follow strict rules on accessing, analysing and processing data. Only aggregate data will leave the CSU. All small numbers will be suppressed before any data is made visible to customers outside of the organisation. Small numbers will be suppressed in line with the HES analysis guide. Pseudonymised, rather than anonymised, data is required to enable calculation of benchmarked metrics on a per patient basis e.g. average number of A&E attendances per patient. For clarity, this request is for non-identifiable, pseudonymised data to flow into the data management team of North of England Commissioning Support Unit.	As described in the objectives the outputs will be two-fold: 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality b. Readmissions c. New to review ratios for outpatients d. Procedures of limited clinical value e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions. Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. 2) In addition to self-service dashboards (see above) the applicant will utilize the HES datasets to undertake various analyses both locally and in support of a range of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data would add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. The business intelligence teams within NECS would use the HES data to produce deep-dive reports and analysis on specific projects whilst ensuring small number suppression is followed for all outputs and no data is shared outside the organisation. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level. Future outputs over the next 12 months (some of which is work in progress) Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. In addition to self-service dashboards NECS will continue to utilise the HES datasets to undertake various analyses both locally and in support of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data will add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level.	Utilising HES data for reporting will provide more accurate peer groups for benchmarking purposes rather than simply comparing neighboring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. For example NECS recently delivered a project for NHS England to help improve the treatment of patients with dementia. National figures suggested the dementia registers in GP practices had patients missing. A dementia dashboard was developed within RAIDR to highlight to GP practices and commissioners where practices potentially had patients missing from their register based on secondary care SUS data – this was successful with the number of patients on dementia registers increasing however it could only be undertaken locally as NECS did not hold a national dataset. Another example is where the CSU (on behalf of a local CCG) has undertaken reporting of emergency admission rates with a view to altering patient pathways. Emergency admission rates for local providers have been used to identify best practice and pathways altered to reduce admissions. However this exercise was limited to local hospitals as NECS did not have national data available. The CCG were then able to compare their local pathways with others where readmission rates were lower with a view to changing how services were configured and commissioned locally. Ideally this would have allowed the CCG to compare their readmission rates with all commissioners/providers nationally but this was not possible without access to the full HES dataset. When the CSU come to extend/renew their agreement, evidence will be supplied for benefits achieved through the provision of dashboards/analysis to each type of customer organisation. What has been achieved - Considerable benchmarking work across the North East to look for QIPP savings, and the ability to drill down into the datasets provided by HES to understand variation at low levels of granularity. However, even if we managed to bring every CCG down to the level of the best in the North East we’d still be outliers nationally, as the North East is a national outlier. To this end, HES allows us to place the North East into context with other sub-regions from across the country, using identical queries, to help to explain some of the limitations of QIPP schemes, but also some of the true opportunities. Utilising HES data for reporting provides more accurate peer groups for benchmarking purposes rather than simply comparing neighbouring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. This is a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. NECS have identified areas of potential savings as in RightCare focus packs, and been able to validate, benchmark and investigate further. E.g. if a CCG has been identified as an outlier for spend on a certain procedure, are activity levels also high or is it just the procedure that is expensive? How do they compare nationally and against their 10 similar CCGs? Is the provider itself an outlier? And are there other similar procedures where underperformance cancels out the potential savings. This gives the CCG confidence in discussions with providers, as more often than not the figures in the RightCare focus packs are questioned by the trusts. A RAIDR dashboard has been developed which supports the Rightcare approach this is currently in testing phase. Using HES NECS have done national comparisons against peer groups to the levels that Right Care don’t do, do more deep dive reporting into the key areas of overspend, provide comparative analysis in our routine reporting against peer CCGs, and generally make use of best national information resource. It has facilitated our CCGs to make contact with “best in class” CCGs, identified through the interrogation of HES, who have, for example, the lowest activity rates in some of the areas we’ve been looking at. This has resulted in useful discussions with those CCGs about their activity levels. Deep-dives have been completed for individual commissioners who want to understand how they compare to other areas to help support whole provider and health economy analysis where service reconfigurations are being proposed. NECS have utilised HES data to inform baseline capacity and demand positions for the acute sector in STPs in our footprint. In particular, one of our STPs which is well progressed, is utilising HES data to inform a capacity planning model to forecast future demand and produce scenarios for future hospital configuration. This STP includes a CCG that is out with our traditional CCG footprint, therefore the HES data provides a consistent, baseline from which this modelling can be carried out. NECS are carrying out a national project to test the effect of a range of factors on patient’s decision to attend A&E departments. In order to derive the independent variable for this model, HES was used to calculate A&E Attendance rates for each of the 5000+ GP Practices within the study. In order to provide context for the North East and Cumbria’s Urgent and Emergency Care 5 Year Strategy, HES has been used to calculate A&E Attendance, Emergency Admission and Emergency Bed Day rates for all CCGs in England in order to produce funnel plots to highlight which CCGs within the network footprint are statistical outliers and provide evidence for action within the strategy. Future benefits over the next 12 months (some of which is work in progress) 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality. NECS were previously reliant on third party commercial products in order to produce mortality reports for our local CCGs. The availability of HES is allowing us to calculate our own mortality indices (for example HSMR) and this is currently being incorporated within our Quality & Performance dashboard within RAIDR. In addition the functionality of RAIDR is such that further deep dives, the ability to compare with Trust peer groups nationally and modifying parameters within the mortality calculations (e.g. looking at 7 day, 30 day, 60 day timeframes) will provide additional intelligence to our health system on hospital mortality. b. Readmissions. Although nationally published statistics are published periodically on readmission rates, these are often at a relatively high level of aggregation, often with a significant time-lag and working to a definition that we don’t believe is necessarily the most appropriate for the types of insight that we are attempting to provide. Within NECS we have the ability for our commissioners to drill down to more appropriate levels of granularity, in a more timely fashion and to our own, locally developed and agreed definition. However, we can provide better comparative analysis and therefore more insight by running similar queries for commissioners within our CCG peer groups, but outwith the North East. Furthermore we are currently producing similar analyses for neighbouring CCGs who do not have access to the skills or underlying data to produce this. c. New to review ratios for outpatients d. Procedures of limited clinical value Within RAIDR NECS currently report on the volume of activity commissioned by each of our CCGs which a part of the Procedures of Limited Clinical Value policy, which is updated each year. There are of course exclusions applied to each of these and using a combination of procedure and diagnosis codes this reports provides intelligence on the number of procedures that are still performed. Being able to apply our algorithm to other commissioners outside of our area will allow NECS to identify where perhaps other commissioners are applying similar rules more stringently, so that NECS can learn from them, and also where other areas are not restricting particular procedures which might lead us to change our policy. e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions.
NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	NHS NORTH OF ENGLAND COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS North of England Commissioning Support (NECS) provides a comprehensive business intelligence (BI) service to a wide range of NHS organisations. This includes both standard analytics and reporting, and deep-dives and diagnostic exercises to offer insight and intelligence on a commissioner’s health economy. In addition NECS offer a mature business intelligence application (RAIDR) allowing self-service access to a range of dashboards and configurable reports. This tool is available on a subscription basis only to NHS organisations and local authorities. It is currently used by Clinical Commissioning Groups (CCGs), internally within the CSU through specialist support teams and by CCG member practices. A list of current customers is attached as a supporting document (SD1). Both the business intelligence team and RAIDR utilise data feeds from secondary and community care, mental health services, urgent and primary care, prescribing and other HSCIC published datasets such as QOF, RTT etc. These data sets are provided either by the DSCRO service, downloaded directly the NHS England data catalogue or directly from provider organisations. Data delivered via the DSCRO is pseudonymised in to the CSU where the BI service and RAIDR are hosted. Published data is downloaded in aggregate form from HSCIC and NHS England websites. There will be no direct linkage between HES data records and other data already used by the CSU and in its BI tool (RAIDR). HES data may be presented alongside other data but not linked to it – for example a report may contain HES data alongside workforce statistics, weather reports etc. Typical uses for the business intelligence service and tool include • Provision of contract, performance and quality monitoring of commissioned services – this ensures CCGs are empowered with intelligence on the services they are accountable for and can undertake their statutory duties • Fully embrace clinical commissioning – CCGs have taken steps to delegate their some of their commissioning responsibility to their member practices. The RAIDR tool is used to present practice level activity and performance information allowing GP practices to assess how their local initiatives affect wider service utilization. For example, does opening their surgery later in the evening reduce the burden on A&E? Can they evidence a change in A&E usage from the point they opened later? • The national drive to progress the Better Care Fund and Vanguard alternate care models both require in-depth analytics. Access to timely information showing the impact of service transformations is key to evidencing the success of these national NHS programmes. • To support the on-going budgetary pressures the NHS is faced the business intelligence service and RAIDR offer significant support to commissioners on their QIPP programmes. Identifying service areas where the commissioner is an outlier that may then require re-procurement of a clinical service, comparisons with peer groups and best practice to understand how a change in approach might deliver a financial saving NECS is requesting HES data that will enable users of their business intelligence service and the RAIDR tool to compare themselves on a national footprint. For benchmarking, national data is needed to allow comparisons due to the number of customer organisations and number of types of organisations. This would be a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. The HES data will be utilized within RAIDR to provide a range of benchmarking dashboards and reports as required to address customer specific priorities. This may include mortality, end of life, procedures of limited clinical value, new to review ratios, readmissions etc. The ability to present a national and peer-group picture of locally defined indicators is the ambition. HES data will be presented independently of existing data flows within a bespoke dashboard as well as to supplement current reports/dashboards. For example using HES to calculate a national readmission rate to be presented on a locally fed readmission report. As well as within the RAIDR tool HES data will be used by the BI team for bespoke analytics and reporting. This will include for individual commissioners who have requested a deep-dive for a particular area and want to understand how they compare to other areas. It will also help support whole provider and health economy analysis where service re-configurations are being proposed. [Note: this is not possible with the commissioner specific slices of data provisioned via the DSCRO]	1. Data will be received and stored by the data management service within NECS. This is a dedicated team responsible for the organisations data warehouses and incoming/outgoing flows of data. The HES datasets will initially land in the teams secure file share before being uploaded in to an SQL Server data warehouse. Both file share and SQL server data are securely hosted within a commercial grade data centre. 2. The data management service will create derived fields based on the data received such as Ambulatory care sensitive condition flag, procedure of limited clinical value flag etc. 3. Data will be used to populate secure data cubes for use by analysts within the CSU. The data being made available will be record level but no identifiable data will be included. Only the minimum required data fields will be used to populate each cube. 4. Data will be used by the RAIDR support team to populate the relevant dashboards and reports within the RAIDR system. No patient level data will be available to RAIDR users. Small number suppression rules will be adhered to. 5. Record level HES data will not be directly linked to any other dataset. Staff follow strict rules on accessing, analysing and processing data. Only aggregate data will leave the CSU. All small numbers will be suppressed before any data is made visible to customers outside of the organisation. Small numbers will be suppressed in line with the HES analysis guide. Pseudonymised, rather than anonymised, data is required to enable calculation of benchmarked metrics on a per patient basis e.g. average number of A&E attendances per patient. For clarity, this request is for non-identifiable, pseudonymised data to flow into the data management team of North of England Commissioning Support Unit.	As described in the objectives the outputs will be two-fold: 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality b. Readmissions c. New to review ratios for outpatients d. Procedures of limited clinical value e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions. Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. 2) In addition to self-service dashboards (see above) the applicant will utilize the HES datasets to undertake various analyses both locally and in support of a range of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data would add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. The business intelligence teams within NECS would use the HES data to produce deep-dive reports and analysis on specific projects whilst ensuring small number suppression is followed for all outputs and no data is shared outside the organisation. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level. Future outputs over the next 12 months (some of which is work in progress) Outputs will include additions to graphs/charts showing a national and peer group figure and also tables detailing how each commissioner compares to others. In addition to self-service dashboards NECS will continue to utilise the HES datasets to undertake various analyses both locally and in support of national projects. Having the full catchment/provider data (commissioners generally only have their registered population) will facilitate accurate modelling of services and a view of complete patient pathways. Current projects where HES data will add significant value to the CSU’s services include: a. Supporting CCG vanguard: NECS is providing support to a number of vanguards, validating their activity/financial models and plans. Not having direct access to a standardized national dataset limits the support that can be provided. b. Service and pathway transformation: redesigning care pathways on behalf of CCGs requires access to activity data covering the entire provider with HES the only source for this. Commissioning plans must be based on accurate and complete information. c. Future commissioning architecture: Having a comprehensive dataset covering the local population will allow the CSU to support local and national STPs as they evolve and transform care at a local level.	Utilising HES data for reporting will provide more accurate peer groups for benchmarking purposes rather than simply comparing neighboring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. For example NECS recently delivered a project for NHS England to help improve the treatment of patients with dementia. National figures suggested the dementia registers in GP practices had patients missing. A dementia dashboard was developed within RAIDR to highlight to GP practices and commissioners where practices potentially had patients missing from their register based on secondary care SUS data – this was successful with the number of patients on dementia registers increasing however it could only be undertaken locally as NECS did not hold a national dataset. Another example is where the CSU (on behalf of a local CCG) has undertaken reporting of emergency admission rates with a view to altering patient pathways. Emergency admission rates for local providers have been used to identify best practice and pathways altered to reduce admissions. However this exercise was limited to local hospitals as NECS did not have national data available. The CCG were then able to compare their local pathways with others where readmission rates were lower with a view to changing how services were configured and commissioned locally. Ideally this would have allowed the CCG to compare their readmission rates with all commissioners/providers nationally but this was not possible without access to the full HES dataset. When the CSU come to extend/renew their agreement, evidence will be supplied for benefits achieved through the provision of dashboards/analysis to each type of customer organisation. What has been achieved - Considerable benchmarking work across the North East to look for QIPP savings, and the ability to drill down into the datasets provided by HES to understand variation at low levels of granularity. However, even if we managed to bring every CCG down to the level of the best in the North East we’d still be outliers nationally, as the North East is a national outlier. To this end, HES allows us to place the North East into context with other sub-regions from across the country, using identical queries, to help to explain some of the limitations of QIPP schemes, but also some of the true opportunities. Utilising HES data for reporting provides more accurate peer groups for benchmarking purposes rather than simply comparing neighbouring commissioners as happens now using DSCRO supplied data. Benchmarking is currently restricted to data held by the local DSCRO so CCGs cannot be compared against their identifier peers as these are often elsewhere in the country. This is a major improvement on the current position where the service is limited to the data flows from the local DSCRO and published data that often is not granular enough to meet user requirements. NECS have identified areas of potential savings as in RightCare focus packs, and been able to validate, benchmark and investigate further. E.g. if a CCG has been identified as an outlier for spend on a certain procedure, are activity levels also high or is it just the procedure that is expensive? How do they compare nationally and against their 10 similar CCGs? Is the provider itself an outlier? And are there other similar procedures where underperformance cancels out the potential savings. This gives the CCG confidence in discussions with providers, as more often than not the figures in the RightCare focus packs are questioned by the trusts. A RAIDR dashboard has been developed which supports the Rightcare approach this is currently in testing phase. Using HES NECS have done national comparisons against peer groups to the levels that Right Care don’t do, do more deep dive reporting into the key areas of overspend, provide comparative analysis in our routine reporting against peer CCGs, and generally make use of best national information resource. It has facilitated our CCGs to make contact with “best in class” CCGs, identified through the interrogation of HES, who have, for example, the lowest activity rates in some of the areas we’ve been looking at. This has resulted in useful discussions with those CCGs about their activity levels. Deep-dives have been completed for individual commissioners who want to understand how they compare to other areas to help support whole provider and health economy analysis where service reconfigurations are being proposed. NECS have utilised HES data to inform baseline capacity and demand positions for the acute sector in STPs in our footprint. In particular, one of our STPs which is well progressed, is utilising HES data to inform a capacity planning model to forecast future demand and produce scenarios for future hospital configuration. This STP includes a CCG that is out with our traditional CCG footprint, therefore the HES data provides a consistent, baseline from which this modelling can be carried out. NECS are carrying out a national project to test the effect of a range of factors on patient’s decision to attend A&E departments. In order to derive the independent variable for this model, HES was used to calculate A&E Attendance rates for each of the 5000+ GP Practices within the study. In order to provide context for the North East and Cumbria’s Urgent and Emergency Care 5 Year Strategy, HES has been used to calculate A&E Attendance, Emergency Admission and Emergency Bed Day rates for all CCGs in England in order to produce funnel plots to highlight which CCGs within the network footprint are statistical outliers and provide evidence for action within the strategy. Future benefits over the next 12 months (some of which is work in progress) 1) Additional dashboards and reports available via the RAIDR tool to registered users covering a range of benchmarking and comparative analysis. This will include a focus on: a. Mortality. NECS were previously reliant on third party commercial products in order to produce mortality reports for our local CCGs. The availability of HES is allowing us to calculate our own mortality indices (for example HSMR) and this is currently being incorporated within our Quality & Performance dashboard within RAIDR. In addition the functionality of RAIDR is such that further deep dives, the ability to compare with Trust peer groups nationally and modifying parameters within the mortality calculations (e.g. looking at 7 day, 30 day, 60 day timeframes) will provide additional intelligence to our health system on hospital mortality. b. Readmissions. Although nationally published statistics are published periodically on readmission rates, these are often at a relatively high level of aggregation, often with a significant time-lag and working to a definition that we don’t believe is necessarily the most appropriate for the types of insight that we are attempting to provide. Within NECS we have the ability for our commissioners to drill down to more appropriate levels of granularity, in a more timely fashion and to our own, locally developed and agreed definition. However, we can provide better comparative analysis and therefore more insight by running similar queries for commissioners within our CCG peer groups, but outwith the North East. Furthermore we are currently producing similar analyses for neighbouring CCGs who do not have access to the skills or underlying data to produce this. c. New to review ratios for outpatients d. Procedures of limited clinical value Within RAIDR NECS currently report on the volume of activity commissioned by each of our CCGs which a part of the Procedures of Limited Clinical Value policy, which is updated each year. There are of course exclusions applied to each of these and using a combination of procedure and diagnosis codes this reports provides intelligence on the number of procedures that are still performed. Being able to apply our algorithm to other commissioners outside of our area will allow NECS to identify where perhaps other commissioners are applying similar rules more stringently, so that NECS can learn from them, and also where other areas are not restricting particular procedures which might lead us to change our policy. e. Falls f. Frequent flyers Allowing commissioners to compare the impact of their programmes and work streams against peer groups and nationally will help determine their effectiveness and inform future commissioning decisions.
NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS South Central and West Clinical Support Unit has previously had access to HDIS but this was suspended on 1st July 2016 due to heightened concern as an NHS Digital investigation had found that South Central and West CSU had downloaded HES pseudonymised patient record level data from the HDIS portal and forwarded this to another NHS Organisation under a Memorandum of Understanding. A Data Sharing Audit was carried out on the 7th and 8th July 2016 and a further Data Sharing Audit was carried out on 14th and 15th December 2016. Both Audit Reports have now been published and post-audit reports shared with NHS Digital. NHS Digital have confirmed that the majority of the actions raised for both of the audits are complete or in the process of being completed. Link to first audit report: http://content.digital.nhs.uk/media/22969/Data-Sharing-Agreement-Audit-Report-NHS-South-Central-and-West-CSU/pdf/Data_Sharing_Agreement_Audit_Report_NHS_South_Central_and_West_CSU(1).pdf Link to second audit report: http://content.digital.nhs.uk/media/24958/Data-Sharing-Agreement-Audit---South-Central-and-West--CSU/pdf/Data_Sharing_Agreement_Audit_-_NHS_South_Central_and_West_CSU.pdf NHS South Central and West Clinical Support Unit (CSU) require Hospital Episode Statistics (HES) data in order to support commissioning for NHS England and Clinical Commissioning Groups (CCG). Recipients of reports containing HES analysis would include CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks. This is because support is currently needed across a large number of work programmes. All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide. The work areas, for which support is requested from the NHS South Central and West Clinical Support Unit by the organisations listed above, are largely nationally recognised, and include the Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP), patient safety, service evaluation and transformation, planning required and general benchmarking analysis. The aims of the projects for which HES reports are required include improving patient pathways, finding savings, planning for the future, improving the efficiency of the local health economies and reducing pressure on hospitals. NHS South Central and West CSU require access to HES data because there is a need to examine data for organisations other than for those within the geographical/commissioning area of our organisation in order to provide the best solutions to the problems presented by our requesting organisations. NHS South Central and West CSU require data for other CCGs nationally in order to benchmark local CCGs against those CCGs which are not in this area, for example the similar 10 Right Care CCGs. NHS South Central and West CSU also require data for other CCGs in the country for projects on clinical areas such as Stroke, Cancer and Sepsis in order to provide intelligence to Academic Science Networks. NHS South Central and West CSU also require data for other NHS Trusts nationally in order to see a full picture of the activity at that Trust as opposed to just a view of that activity locally commissioned. NHS Trust benchmarking has also been requested to support STPs. HES data is also needed when an organisation wants to know what is beneath the figures in nationally published data which has been produced using Hospital Episodes Statistics, such as the cause of high activity or spend. Availability of HES means that NHS South Central and West CSU can replicate the national query and know that NHS South Central and West CSU are extracting data on the same basis. It also means that NHS South Central and West CSU can produce monthly updates on the performance against national measures which may only be updated annually at national level. An example of this is the monthly monitoring of some of the Right Care measures for the Quality Premium. Use of the data will also include trend analysis.	NHS South Central and West CSU will be required for every extract of data that they require to write a query, (defining and limiting the criteria) explaining the purpose for the query and provide this to NHS Digital. NHS Digital will review the query, taking the purpose into consideration and if approved run the query and provide the aggregated output to NHS South Central West CSU. The aggregated output may contain small numbers and it is made clear with each output received that small numbers must be suppressed in line with the HES analysis guide. NHS South Central and West CSU would not flow the data to any other system or database and would not flow data to NHS Digital. NHS South Central and West CSU will produce, from the outputs provided, tables or graphs. The data will not be made available to any third parties. CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks would receive outputs in the form of aggregated data with small numbers suppressed in line with the HES Analysis Guide. Work will not be carried out for any other organisation type without an amendment to this Agreement. Data will only be accessed by individuals within South Central and West CSU who have authorisation from South Central and West CSU to access the data for the purposes described, all of whom are substantive employees of South Central and West CSU. The HES outputs will only be accessed by individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. The data will not be linked with any record level data. There will be no requirement, nor attempt, to re-identify individuals from the data. Access to HDIS will be restricted to approved users agreed with the HSCIC in a controlled manner. Initially, 2 user licences are approved and this will be managed under change control. The charges outlined in this agreement may therefore vary over the agreement period. An annual review of the system use will be completed as part of the audit process. NHS Digital will monitor use of the HDIS2 system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	Most of the analysis undertaken is ad hoc on a continuous basis. Projects undertaken either result from a direct request from a customer for example a recent request to produce a graph showing rates of emergency admissions for all CCGs) or as part of their Service Level Agreement with the CSU to provide a benchmarking service. An example of work carried out under the benchmarking service would be creating a tool which shows outpatient first to follow up ratio for a similar 10 CCGs. The Service Level Agreement sets out the type of work which will be carried out by the CSU against the purpose/benefit to the organisation in question. For example the SLA sets out that national or local benchmarking will be produced to answer the question under section 2.1 the commissioning model “How Healthy? Reducing health inequalities and improving health outcomes now and in the future”. Tools are created which meet a commissioning intelligence objective which are set out in the Service Level Agreement and these will be shared with organisations who have signed up to this agreement. The CSU does not undertake any analysis for an organisation unless it is specifically requested or unless the organisation has entered into a Service Level Agreement in which the nature of the work the CSU will perform in the future is set out against specific objectives (which are for monitoring or improving healthcare) for that customer. The outputs would include analysis on Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP ), patient safety, service evaluation and transformation, patient pathway analysis, planning and general benchmarking analysis. Recipients of reports containing HES analysis would include Clinical Commissioning Groups (local as well as those in other regions), NHS Trusts, Area Teams, NHS England, Vanguards, STPs, Local Authorities and Academic Science Networks. The reports seen by these organisations would contain aggregated data with small numbers suppressed in line with the HES Analysis Guide.	The availability of national data enables the organisations to benchmark themselves with similar organisations nationally and highlight any variations and potential for improvement. The findings can support decisions undertaken by those organisations (for example, on whether to invest in a new service) and lead to better use of resources. As an example, South Central and West CSU used HES data to support the Right Care Programme Deep Dive for Northern, Eastern and NEW Devon. NHS South Central and West CSU produced reports based on insight from HES data showing savings opportunities. This enables NHS South Central and West CSU to identify specific HRGs and specific savings for NEW Devon by comparing the spread of NEW Devon Health Resource Groups activity with that of the best 5 similar CCGs. For example opportunities for reductions in tonsillectomies were identified which could save significant amounts of money and will benefit patients in reducing the need for surgery (and instead of using other methods of treatment). Demand for benchmarking of the Right Care similar CCGs is on-going as Right Care continues to be implemented. The CCGs in the area have found that the analysis produced using HES data gives confidence in the selection of QIPP schemes and service development by providing examples of the position in other similar CCGs. Use of national benchmarking can demonstrate that a CCG has been thorough in the planning for the new contract year. For example NHS South Central and West CSU undertook benchmarking for NHS Surrey Heath CCG against their 10 similar CCGs using HES data, which enabled QIPP savings of several thousand pounds in the redesign of hip and knee pathways to be identified. The analysis was also used for other schemes including stroke, diabetes, angina, heart failure and ophthalmology, covering emergency and planned care. From a commissioning perspective, the ability to use HES data will ensure that an organisation’s commissioning needs are met, which, as set out in the NHS England Commissioning Intelligence Model, include 2.1 (how healthy?) reducing health inequalities and improving health outcomes now and in the future, 2.2 (what is really happening in this system?)identifying duplication and improving integration of care, 2.4 (how do we compare?) showing how they compare to other organisations to challenge the current state and improve clinical outcomes, and 2.6 (how could things be better?) developing new pathways and/or decommissioning services to improve safety, quality and, effectiveness of care
NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS South Central and West Clinical Support Unit has previously had access to HDIS but this was suspended on 1st July 2016 due to heightened concern as an NHS Digital investigation had found that South Central and West CSU had downloaded HES pseudonymised patient record level data from the HDIS portal and forwarded this to another NHS Organisation under a Memorandum of Understanding. A Data Sharing Audit was carried out on the 7th and 8th July 2016 and a further Data Sharing Audit was carried out on 14th and 15th December 2016. Both Audit Reports have now been published and post-audit reports shared with NHS Digital. NHS Digital have confirmed that the majority of the actions raised for both of the audits are complete or in the process of being completed. Link to first audit report: http://content.digital.nhs.uk/media/22969/Data-Sharing-Agreement-Audit-Report-NHS-South-Central-and-West-CSU/pdf/Data_Sharing_Agreement_Audit_Report_NHS_South_Central_and_West_CSU(1).pdf Link to second audit report: http://content.digital.nhs.uk/media/24958/Data-Sharing-Agreement-Audit---South-Central-and-West--CSU/pdf/Data_Sharing_Agreement_Audit_-_NHS_South_Central_and_West_CSU.pdf NHS South Central and West Clinical Support Unit (CSU) require Hospital Episode Statistics (HES) data in order to support commissioning for NHS England and Clinical Commissioning Groups (CCG). Recipients of reports containing HES analysis would include CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks. This is because support is currently needed across a large number of work programmes. All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide. The work areas, for which support is requested from the NHS South Central and West Clinical Support Unit by the organisations listed above, are largely nationally recognised, and include the Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP), patient safety, service evaluation and transformation, planning required and general benchmarking analysis. The aims of the projects for which HES reports are required include improving patient pathways, finding savings, planning for the future, improving the efficiency of the local health economies and reducing pressure on hospitals. NHS South Central and West CSU require access to HES data because there is a need to examine data for organisations other than for those within the geographical/commissioning area of our organisation in order to provide the best solutions to the problems presented by our requesting organisations. NHS South Central and West CSU require data for other CCGs nationally in order to benchmark local CCGs against those CCGs which are not in this area, for example the similar 10 Right Care CCGs. NHS South Central and West CSU also require data for other CCGs in the country for projects on clinical areas such as Stroke, Cancer and Sepsis in order to provide intelligence to Academic Science Networks. NHS South Central and West CSU also require data for other NHS Trusts nationally in order to see a full picture of the activity at that Trust as opposed to just a view of that activity locally commissioned. NHS Trust benchmarking has also been requested to support STPs. HES data is also needed when an organisation wants to know what is beneath the figures in nationally published data which has been produced using Hospital Episodes Statistics, such as the cause of high activity or spend. Availability of HES means that NHS South Central and West CSU can replicate the national query and know that NHS South Central and West CSU are extracting data on the same basis. It also means that NHS South Central and West CSU can produce monthly updates on the performance against national measures which may only be updated annually at national level. An example of this is the monthly monitoring of some of the Right Care measures for the Quality Premium. Use of the data will also include trend analysis.	NHS South Central and West CSU will be required for every extract of data that they require to write a query, (defining and limiting the criteria) explaining the purpose for the query and provide this to NHS Digital. NHS Digital will review the query, taking the purpose into consideration and if approved run the query and provide the aggregated output to NHS South Central West CSU. The aggregated output may contain small numbers and it is made clear with each output received that small numbers must be suppressed in line with the HES analysis guide. NHS South Central and West CSU would not flow the data to any other system or database and would not flow data to NHS Digital. NHS South Central and West CSU will produce, from the outputs provided, tables or graphs. The data will not be made available to any third parties. CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks would receive outputs in the form of aggregated data with small numbers suppressed in line with the HES Analysis Guide. Work will not be carried out for any other organisation type without an amendment to this Agreement. Data will only be accessed by individuals within South Central and West CSU who have authorisation from South Central and West CSU to access the data for the purposes described, all of whom are substantive employees of South Central and West CSU. The HES outputs will only be accessed by individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. The data will not be linked with any record level data. There will be no requirement, nor attempt, to re-identify individuals from the data. Access to HDIS will be restricted to approved users agreed with the HSCIC in a controlled manner. Initially, 2 user licences are approved and this will be managed under change control. The charges outlined in this agreement may therefore vary over the agreement period. An annual review of the system use will be completed as part of the audit process. NHS Digital will monitor use of the HDIS2 system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	Most of the analysis undertaken is ad hoc on a continuous basis. Projects undertaken either result from a direct request from a customer for example a recent request to produce a graph showing rates of emergency admissions for all CCGs) or as part of their Service Level Agreement with the CSU to provide a benchmarking service. An example of work carried out under the benchmarking service would be creating a tool which shows outpatient first to follow up ratio for a similar 10 CCGs. The Service Level Agreement sets out the type of work which will be carried out by the CSU against the purpose/benefit to the organisation in question. For example the SLA sets out that national or local benchmarking will be produced to answer the question under section 2.1 the commissioning model “How Healthy? Reducing health inequalities and improving health outcomes now and in the future”. Tools are created which meet a commissioning intelligence objective which are set out in the Service Level Agreement and these will be shared with organisations who have signed up to this agreement. The CSU does not undertake any analysis for an organisation unless it is specifically requested or unless the organisation has entered into a Service Level Agreement in which the nature of the work the CSU will perform in the future is set out against specific objectives (which are for monitoring or improving healthcare) for that customer. The outputs would include analysis on Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP ), patient safety, service evaluation and transformation, patient pathway analysis, planning and general benchmarking analysis. Recipients of reports containing HES analysis would include Clinical Commissioning Groups (local as well as those in other regions), NHS Trusts, Area Teams, NHS England, Vanguards, STPs, Local Authorities and Academic Science Networks. The reports seen by these organisations would contain aggregated data with small numbers suppressed in line with the HES Analysis Guide.	The availability of national data enables the organisations to benchmark themselves with similar organisations nationally and highlight any variations and potential for improvement. The findings can support decisions undertaken by those organisations (for example, on whether to invest in a new service) and lead to better use of resources. As an example, South Central and West CSU used HES data to support the Right Care Programme Deep Dive for Northern, Eastern and NEW Devon. NHS South Central and West CSU produced reports based on insight from HES data showing savings opportunities. This enables NHS South Central and West CSU to identify specific HRGs and specific savings for NEW Devon by comparing the spread of NEW Devon Health Resource Groups activity with that of the best 5 similar CCGs. For example opportunities for reductions in tonsillectomies were identified which could save significant amounts of money and will benefit patients in reducing the need for surgery (and instead of using other methods of treatment). Demand for benchmarking of the Right Care similar CCGs is on-going as Right Care continues to be implemented. The CCGs in the area have found that the analysis produced using HES data gives confidence in the selection of QIPP schemes and service development by providing examples of the position in other similar CCGs. Use of national benchmarking can demonstrate that a CCG has been thorough in the planning for the new contract year. For example NHS South Central and West CSU undertook benchmarking for NHS Surrey Heath CCG against their 10 similar CCGs using HES data, which enabled QIPP savings of several thousand pounds in the redesign of hip and knee pathways to be identified. The analysis was also used for other schemes including stroke, diabetes, angina, heart failure and ophthalmology, covering emergency and planned care. From a commissioning perspective, the ability to use HES data will ensure that an organisation’s commissioning needs are met, which, as set out in the NHS England Commissioning Intelligence Model, include 2.1 (how healthy?) reducing health inequalities and improving health outcomes now and in the future, 2.2 (what is really happening in this system?)identifying duplication and improving integration of care, 2.4 (how do we compare?) showing how they compare to other organisations to challenge the current state and improve clinical outcomes, and 2.6 (how could things be better?) developing new pathways and/or decommissioning services to improve safety, quality and, effectiveness of care
NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	NHS SOUTH, CENTRAL AND WEST COMMISSIONING SUPPORT UNIT	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	NHS South Central and West Clinical Support Unit has previously had access to HDIS but this was suspended on 1st July 2016 due to heightened concern as an NHS Digital investigation had found that South Central and West CSU had downloaded HES pseudonymised patient record level data from the HDIS portal and forwarded this to another NHS Organisation under a Memorandum of Understanding. A Data Sharing Audit was carried out on the 7th and 8th July 2016 and a further Data Sharing Audit was carried out on 14th and 15th December 2016. Both Audit Reports have now been published and post-audit reports shared with NHS Digital. NHS Digital have confirmed that the majority of the actions raised for both of the audits are complete or in the process of being completed. Link to first audit report: http://content.digital.nhs.uk/media/22969/Data-Sharing-Agreement-Audit-Report-NHS-South-Central-and-West-CSU/pdf/Data_Sharing_Agreement_Audit_Report_NHS_South_Central_and_West_CSU(1).pdf Link to second audit report: http://content.digital.nhs.uk/media/24958/Data-Sharing-Agreement-Audit---South-Central-and-West--CSU/pdf/Data_Sharing_Agreement_Audit_-_NHS_South_Central_and_West_CSU.pdf NHS South Central and West Clinical Support Unit (CSU) require Hospital Episode Statistics (HES) data in order to support commissioning for NHS England and Clinical Commissioning Groups (CCG). Recipients of reports containing HES analysis would include CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks. This is because support is currently needed across a large number of work programmes. All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide. The work areas, for which support is requested from the NHS South Central and West Clinical Support Unit by the organisations listed above, are largely nationally recognised, and include the Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP), patient safety, service evaluation and transformation, planning required and general benchmarking analysis. The aims of the projects for which HES reports are required include improving patient pathways, finding savings, planning for the future, improving the efficiency of the local health economies and reducing pressure on hospitals. NHS South Central and West CSU require access to HES data because there is a need to examine data for organisations other than for those within the geographical/commissioning area of our organisation in order to provide the best solutions to the problems presented by our requesting organisations. NHS South Central and West CSU require data for other CCGs nationally in order to benchmark local CCGs against those CCGs which are not in this area, for example the similar 10 Right Care CCGs. NHS South Central and West CSU also require data for other CCGs in the country for projects on clinical areas such as Stroke, Cancer and Sepsis in order to provide intelligence to Academic Science Networks. NHS South Central and West CSU also require data for other NHS Trusts nationally in order to see a full picture of the activity at that Trust as opposed to just a view of that activity locally commissioned. NHS Trust benchmarking has also been requested to support STPs. HES data is also needed when an organisation wants to know what is beneath the figures in nationally published data which has been produced using Hospital Episodes Statistics, such as the cause of high activity or spend. Availability of HES means that NHS South Central and West CSU can replicate the national query and know that NHS South Central and West CSU are extracting data on the same basis. It also means that NHS South Central and West CSU can produce monthly updates on the performance against national measures which may only be updated annually at national level. An example of this is the monthly monitoring of some of the Right Care measures for the Quality Premium. Use of the data will also include trend analysis.	NHS South Central and West CSU will be required for every extract of data that they require to write a query, (defining and limiting the criteria) explaining the purpose for the query and provide this to NHS Digital. NHS Digital will review the query, taking the purpose into consideration and if approved run the query and provide the aggregated output to NHS South Central West CSU. The aggregated output may contain small numbers and it is made clear with each output received that small numbers must be suppressed in line with the HES analysis guide. NHS South Central and West CSU would not flow the data to any other system or database and would not flow data to NHS Digital. NHS South Central and West CSU will produce, from the outputs provided, tables or graphs. The data will not be made available to any third parties. CCGs (local as well as those in other regions), NHS Trusts, Area Teams, and NHS England, Vanguards, Sustainability and Transformation Plans organisation (STPs), Local Authorities, Patient Safety Collaborates Accountable Care Organisations, NHS Improvement and Academic Science Networks would receive outputs in the form of aggregated data with small numbers suppressed in line with the HES Analysis Guide. Work will not be carried out for any other organisation type without an amendment to this Agreement. Data will only be accessed by individuals within South Central and West CSU who have authorisation from South Central and West CSU to access the data for the purposes described, all of whom are substantive employees of South Central and West CSU. The HES outputs will only be accessed by individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and equivalent controls as substantive employees. The data will not be linked with any record level data. There will be no requirement, nor attempt, to re-identify individuals from the data. Access to HDIS will be restricted to approved users agreed with the HSCIC in a controlled manner. Initially, 2 user licences are approved and this will be managed under change control. The charges outlined in this agreement may therefore vary over the agreement period. An annual review of the system use will be completed as part of the audit process. NHS Digital will monitor use of the HDIS2 system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	Most of the analysis undertaken is ad hoc on a continuous basis. Projects undertaken either result from a direct request from a customer for example a recent request to produce a graph showing rates of emergency admissions for all CCGs) or as part of their Service Level Agreement with the CSU to provide a benchmarking service. An example of work carried out under the benchmarking service would be creating a tool which shows outpatient first to follow up ratio for a similar 10 CCGs. The Service Level Agreement sets out the type of work which will be carried out by the CSU against the purpose/benefit to the organisation in question. For example the SLA sets out that national or local benchmarking will be produced to answer the question under section 2.1 the commissioning model “How Healthy? Reducing health inequalities and improving health outcomes now and in the future”. Tools are created which meet a commissioning intelligence objective which are set out in the Service Level Agreement and these will be shared with organisations who have signed up to this agreement. The CSU does not undertake any analysis for an organisation unless it is specifically requested or unless the organisation has entered into a Service Level Agreement in which the nature of the work the CSU will perform in the future is set out against specific objectives (which are for monitoring or improving healthcare) for that customer. The outputs would include analysis on Better Care Fund, Quality Premium, Vanguard, Sustainability and Transformation Plans, Right Care, Quality, Innovation, Productivity and Prevention (QIPP ), patient safety, service evaluation and transformation, patient pathway analysis, planning and general benchmarking analysis. Recipients of reports containing HES analysis would include Clinical Commissioning Groups (local as well as those in other regions), NHS Trusts, Area Teams, NHS England, Vanguards, STPs, Local Authorities and Academic Science Networks. The reports seen by these organisations would contain aggregated data with small numbers suppressed in line with the HES Analysis Guide.	The availability of national data enables the organisations to benchmark themselves with similar organisations nationally and highlight any variations and potential for improvement. The findings can support decisions undertaken by those organisations (for example, on whether to invest in a new service) and lead to better use of resources. As an example, South Central and West CSU used HES data to support the Right Care Programme Deep Dive for Northern, Eastern and NEW Devon. NHS South Central and West CSU produced reports based on insight from HES data showing savings opportunities. This enables NHS South Central and West CSU to identify specific HRGs and specific savings for NEW Devon by comparing the spread of NEW Devon Health Resource Groups activity with that of the best 5 similar CCGs. For example opportunities for reductions in tonsillectomies were identified which could save significant amounts of money and will benefit patients in reducing the need for surgery (and instead of using other methods of treatment). Demand for benchmarking of the Right Care similar CCGs is on-going as Right Care continues to be implemented. The CCGs in the area have found that the analysis produced using HES data gives confidence in the selection of QIPP schemes and service development by providing examples of the position in other similar CCGs. Use of national benchmarking can demonstrate that a CCG has been thorough in the planning for the new contract year. For example NHS South Central and West CSU undertook benchmarking for NHS Surrey Heath CCG against their 10 similar CCGs using HES data, which enabled QIPP savings of several thousand pounds in the redesign of hip and knee pathways to be identified. The analysis was also used for other schemes including stroke, diabetes, angina, heart failure and ophthalmology, covering emergency and planned care. From a commissioning perspective, the ability to use HES data will ensure that an organisation’s commissioning needs are met, which, as set out in the NHS England Commissioning Intelligence Model, include 2.1 (how healthy?) reducing health inequalities and improving health outcomes now and in the future, 2.2 (what is really happening in this system?)identifying duplication and improving integration of care, 2.4 (how do we compare?) showing how they compare to other organisations to challenge the current state and improve clinical outcomes, and 2.6 (how could things be better?) developing new pathways and/or decommissioning services to improve safety, quality and, effectiveness of care
NON-SPECIALIST ACUTE TRUSTS IN ENGLAND	NON-SPECIALIST ACUTE TRUSTS IN ENGLAND	HES data with fields derived for the calculation of the Summary Hospital-level Mortality Indicator (SHMI)	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	Summary Hospital-level Mortality Indicator (SHMI) record level data and 10 Variable Life Adjusted Display (VLAD) charts provided quarterly (data relating to their own trust only). Recipient signs and returns declaration statement that they are duly authorised by their Caldicott Guardian to receive and share the data as required. As of January 2016, there are 84 trusts registered to receive data from the SHMI Data Extract service.
NORTHGATE PUBLIC SERVICES (UK) LIMITED	NORTHGATE PUBLIC SERVICES (UK) LIMITED	Hospital Episode Statistics Admitted Patient Care	Aggregated-Small Numbers Suppressed	Non Sensitive	Health and Social Care Act 2012	One-Off	N	This is a small numbers suppressed tabulation, with the main objective being that it will provide the applicant with an indication of how patient objections affect NHS Digitals data. This is connected to Northgate's linkage of National Joint Registry data to NHS Digital HES, PROMS and ONS information under a separate agreement, and if it worth pursing the linkage of the cohort in the future. The work will involve the creation of a simple tabulation based on 5 years of HES Admitted Patient Care data showing 'objection counts per year’, split by people removed and episodes removed. This will also include a count of the relevant operations removed.	NHS digital will create a small number suppressed tabulation with HES Admitted Patient Care as the basis, that will show the number of patients removed by the NHS Digital patient objections wash. It will be split into 3 rows - number of patients removed, number of episodes removed and a count of the relevant operations removed.	Once the output has been reviewed, this will enable the application to determine if it is worth considering seeking further legal approvals for the linkage of their cohort to HES, ONS and PROMS data as part of the broader National Joint Registry work.	The benefit is that potentially more patients could be linked to NHS Digitals data if the patient objections figures prove to be positive. This would result in a better quality of data within the National Joint Registry work.
NUVIA LTD	NUVIA LTD	MRIS - Cause of Death Report	Identifiable	Sensitive	Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007	Ongoing	Y	Study MR110 is a long standing study of the effects of occupational radiation exposure in the nuclear industry. Much of the knowledge in this area has been gained from studies of the survivors of the two atomic bombs detonated over Japan at the end of the war. However, it is not clear how relevant this study of high doses received instantaneously is, to those exposed in an occupational context, or as members of the public, to much lower doses received over many years. This is why studies of nuclear workers have been, and continue to be, important in setting acceptable exposure levels at work and for the public in the wider environment. MR110 is a study of mortality and cancer morbidity in the past and present employees of the UK Atomic Energy Authority, the government organisation responsible for the initiation and technical and scientific development of the UK’s civil nuclear energy programme. It is among the small group of UK studies that lead the world in this area partly because of the quality of the national mortality and cancer registration systems. The study commenced in 1979, designed by a team from the London School of Hygiene and Tropical Medicine. The MR110 cohort included everyone ever employed by UKAEA from its foundation in 1946. Since then all new recruits have been added to the study, which now includes around 75,000 individuals. Of these only some 28,000 are dead, which is why continued long-term follow-up, with a corresponding increase in statistical power is so important. The study was initially funded by UKAEA and largely carried out by a team from UKAEA, but with reorganisation and privatisation in the nuclear sector responsibilities have changed and it is now mostly funded by the Nuclear Decommissioning Authority (NDA), overseen by the Public Health England (PHE), Centre for Radiation, Chemical and Environmental Hazards, and carried out by Nuvia Limited. The NDA is an executive non-departmental public body, sponsored by the Department for Business, Energy & Industrial Strategy to ensure the safe and efficient clean-up of the UK’s nuclear legacy (https://www.gov.uk/government/organisations/nuclear-decommissioning-authority) The follow-up data provided by NHS Digital for the MR110 study has been used and will be used in wider collaborative studies taking in the nuclear workforces of several European countries. Such data is, of course, shared in anonymised form with collaborators and only with explicit agreement from the data providers, including NHS Digital, and from workforce representatives. Where data to be shared is anything other than data aggregated with small numbers suppressed the study will seek explicit permission from NHS Digital on a case by case basis via an amendment to the application.	The data requested and already disseminated will be accessed and processed by substantive employees of Nuvia and only for the purposes described in the application. MR110 is a flagging study, so data is provided to NHS Digital by Nuvia to enable the flags to be set. For each individual in the cohort this data comprises, name, date of birth, NHS number, home address, date to address and a unique study identifier. The data requested by Nuvia from NHS Digital falls into three categories: 1. Mortality Data Date, causes and place of death, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number, occupation, home address. 2. Emigrations and Reregistration Date of event, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number. 3. Cancer Registrations Date of diagnosis, disease code, unique study identifier, but no identity data. The study has a favourable ethical opinion from the NHS Research Ethics Committee, Oxford C. It has approval from, and reports to the PHE/NDA Epidemiology Governance Group, which includes employee representatives. Nuvia will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuvia for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide. Nuvia is currently part of a European consortium and is seeking funding to conduct a new study, which would entail data sharing with PHE. Any instances of data sharing or processing of data relating to a new study not outlined in this agreement will be subject to separate applications to NHS Digital. New data subjects are recognised and added to the SHIELD database when their personnel data is sent to the Health Effects team by UKAEA’s contractors CSC, or by DSRL and Magnox. Annually, radiation dose data arrives from the dosimetry services (ADSs) of the same employers and is linked to the data in SHIELD by name, DOB and National Insurance Number (NIN). Periodically the required details for flagging are sent to NHS Digital and Dumfries. When data comes to the study from NHS Digital, either as event notifications or members and postings listings, it is initially linked to the data in the database using name, DOB and NHS Number. Subsequently, if the study are happy that the correct person has been flagged then the study will link on member number. The study have to retain the identity data in the database even after the routine linkage has taken place, because periodically the study add new categories of exposure data. A recent example is the addition of 70 years’ worth of internal radiation contamination assessments to the SHIELD database. SHIELD is an Oracle 12 database, hosted on a server which is only accessible to authorised SHIELD users. Within SHIELD the followup data from NHS Digital is only available to those with the accredited researcher approvals. Data security is an important part of the culture of SHIELD users. Typical analyses would be: • Calculation of Standardised Mortality Ratios (SMRs) and Registration Ratios (SRRs) to compare the cohort with the national population in terms of mortality and cancer morbidity. • Calculation of Rate Ratios to compare radiation workers with non-radiation workers in the nuclear industry. • Tests for trends of mortality and morbidity rates with increasing radiation dose. • Logistic regression analyses to calculate the Excess Relative Risk per unit of radiation dose. ONS Terms and Conditions will be adhered to.	Outputs were presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where appropriate. As part of a previous EU funded project, the SHIELD database has been updated with all the internal dosimetry data required to undertake this work. The outputs included peer-reviewed publications and a publicly available web site. Data from the MR110 cohort has been used in some 20 publications in high-impact peer-reviewed journals. For example: Beral V, Inskip H, Fraser P, Booth M, Coleman D and Rose G (1985) Mortality of employees of the United Kingdom Atomic Energy Authority, 1946-1979. British Medical Journal, 291, 440-447. Fraser, P, Carpenter L, Maconochie N, Higgins C, Booth M and Beral V (1993) Cancer Mortality and morbidity in employees of the United Kingdom Atomic Energy Authority, 1946-86. British Journal of Cancer, 67, 615-624. Rooney C, Beral V, Maconochie N, Fraser P and Davies G (1993) Case-control study of prostatic cancer in employees of the United Kingdom Atomic Energy Authority. British Medical Journal, 307, 1391-1397 Roman E, Doyle P, Maconochie N, Davies G, Smith P and Beral V (1999) Cancer in children of nuclear industry employees: report on children aged under 25 years from nuclear industry family study, British Medical Journal; 318, 1443–1450 Atkinson WD, Law DV, Bromley KJ and Inskip HM (2004) Mortality of employees of the United Kingdom Atomic Energy Authority 1946-97 Occupational & .Environmental Medicine 61, 577-585 Atkinson WD, Law DV, Bromley KJ (2007) A decline in mortality from prostate cancer in the UK Atomic Energy Authority workforce. Journal of Radiation Protection, 27, 437-445 Grellier J, Atkinson, W et al (2016) Risk of lung cancer mortality in nuclear workers from internal exposure to alpha particle-emitting radionuclides. Epidemiology (in press) Future outputs are expected to focus on the health effects of inhaled or ingested radionuclides which have been little studied anywhere hitherto. Outputs will include peer-reviewed publications and a publicly available web site. In all cases data will be presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where necessary.	Studies of the MR110 cohort have influenced and are expected to influence the development of the Ionising Radiation Regulations (IRRs) which regulate the exposure of people at work and of the public. The correct regulation of doses benefits the health not only of nuclear workers, but anyone else who works with radiation, such as medical radiographers and members of the public exposed as a result of medical x-rays or radioactive discharges to the environment. The IRRs are directly based on the authoritative recommendations of organisations such as the International Commission on Radiation Protection (ICRP) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). These organisations monitor the latest research literature and will be aware of past and future publications from MR110. In particular the UNSCEAR 2006 and 2012 reports cited various publications which use MR110 data. No record level data is or will be shared with any other organisation not specified in the application. Any data shared is aggregated with small number suppressed in line with HES Analysis guide.
NUVIA LTD	NUVIA LTD	MRIS - Cohort Event Notification Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Study MR110 is a long standing study of the effects of occupational radiation exposure in the nuclear industry. Much of the knowledge in this area has been gained from studies of the survivors of the two atomic bombs detonated over Japan at the end of the war. However, it is not clear how relevant this study of high doses received instantaneously is, to those exposed in an occupational context, or as members of the public, to much lower doses received over many years. This is why studies of nuclear workers have been, and continue to be, important in setting acceptable exposure levels at work and for the public in the wider environment. MR110 is a study of mortality and cancer morbidity in the past and present employees of the UK Atomic Energy Authority, the government organisation responsible for the initiation and technical and scientific development of the UK’s civil nuclear energy programme. It is among the small group of UK studies that lead the world in this area partly because of the quality of the national mortality and cancer registration systems. The study commenced in 1979, designed by a team from the London School of Hygiene and Tropical Medicine. The MR110 cohort included everyone ever employed by UKAEA from its foundation in 1946. Since then all new recruits have been added to the study, which now includes around 75,000 individuals. Of these only some 28,000 are dead, which is why continued long-term follow-up, with a corresponding increase in statistical power is so important. The study was initially funded by UKAEA and largely carried out by a team from UKAEA, but with reorganisation and privatisation in the nuclear sector responsibilities have changed and it is now mostly funded by the Nuclear Decommissioning Authority (NDA), overseen by the Public Health England (PHE), Centre for Radiation, Chemical and Environmental Hazards, and carried out by Nuvia Limited. The NDA is an executive non-departmental public body, sponsored by the Department for Business, Energy & Industrial Strategy to ensure the safe and efficient clean-up of the UK’s nuclear legacy (https://www.gov.uk/government/organisations/nuclear-decommissioning-authority) The follow-up data provided by NHS Digital for the MR110 study has been used and will be used in wider collaborative studies taking in the nuclear workforces of several European countries. Such data is, of course, shared in anonymised form with collaborators and only with explicit agreement from the data providers, including NHS Digital, and from workforce representatives. Where data to be shared is anything other than data aggregated with small numbers suppressed the study will seek explicit permission from NHS Digital on a case by case basis via an amendment to the application.	The data requested and already disseminated will be accessed and processed by substantive employees of Nuvia and only for the purposes described in the application. MR110 is a flagging study, so data is provided to NHS Digital by Nuvia to enable the flags to be set. For each individual in the cohort this data comprises, name, date of birth, NHS number, home address, date to address and a unique study identifier. The data requested by Nuvia from NHS Digital falls into three categories: 1. Mortality Data Date, causes and place of death, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number, occupation, home address. 2. Emigrations and Reregistration Date of event, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number. 3. Cancer Registrations Date of diagnosis, disease code, unique study identifier, but no identity data. The study has a favourable ethical opinion from the NHS Research Ethics Committee, Oxford C. It has approval from, and reports to the PHE/NDA Epidemiology Governance Group, which includes employee representatives. Nuvia will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuvia for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide. Nuvia is currently part of a European consortium and is seeking funding to conduct a new study, which would entail data sharing with PHE. Any instances of data sharing or processing of data relating to a new study not outlined in this agreement will be subject to separate applications to NHS Digital. New data subjects are recognised and added to the SHIELD database when their personnel data is sent to the Health Effects team by UKAEA’s contractors CSC, or by DSRL and Magnox. Annually, radiation dose data arrives from the dosimetry services (ADSs) of the same employers and is linked to the data in SHIELD by name, DOB and National Insurance Number (NIN). Periodically the required details for flagging are sent to NHS Digital and Dumfries. When data comes to the study from NHS Digital, either as event notifications or members and postings listings, it is initially linked to the data in the database using name, DOB and NHS Number. Subsequently, if the study are happy that the correct person has been flagged then the study will link on member number. The study have to retain the identity data in the database even after the routine linkage has taken place, because periodically the study add new categories of exposure data. A recent example is the addition of 70 years’ worth of internal radiation contamination assessments to the SHIELD database. SHIELD is an Oracle 12 database, hosted on a server which is only accessible to authorised SHIELD users. Within SHIELD the followup data from NHS Digital is only available to those with the accredited researcher approvals. Data security is an important part of the culture of SHIELD users. Typical analyses would be: • Calculation of Standardised Mortality Ratios (SMRs) and Registration Ratios (SRRs) to compare the cohort with the national population in terms of mortality and cancer morbidity. • Calculation of Rate Ratios to compare radiation workers with non-radiation workers in the nuclear industry. • Tests for trends of mortality and morbidity rates with increasing radiation dose. • Logistic regression analyses to calculate the Excess Relative Risk per unit of radiation dose. ONS Terms and Conditions will be adhered to.	Outputs were presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where appropriate. As part of a previous EU funded project, the SHIELD database has been updated with all the internal dosimetry data required to undertake this work. The outputs included peer-reviewed publications and a publicly available web site. Data from the MR110 cohort has been used in some 20 publications in high-impact peer-reviewed journals. For example: Beral V, Inskip H, Fraser P, Booth M, Coleman D and Rose G (1985) Mortality of employees of the United Kingdom Atomic Energy Authority, 1946-1979. British Medical Journal, 291, 440-447. Fraser, P, Carpenter L, Maconochie N, Higgins C, Booth M and Beral V (1993) Cancer Mortality and morbidity in employees of the United Kingdom Atomic Energy Authority, 1946-86. British Journal of Cancer, 67, 615-624. Rooney C, Beral V, Maconochie N, Fraser P and Davies G (1993) Case-control study of prostatic cancer in employees of the United Kingdom Atomic Energy Authority. British Medical Journal, 307, 1391-1397 Roman E, Doyle P, Maconochie N, Davies G, Smith P and Beral V (1999) Cancer in children of nuclear industry employees: report on children aged under 25 years from nuclear industry family study, British Medical Journal; 318, 1443–1450 Atkinson WD, Law DV, Bromley KJ and Inskip HM (2004) Mortality of employees of the United Kingdom Atomic Energy Authority 1946-97 Occupational & .Environmental Medicine 61, 577-585 Atkinson WD, Law DV, Bromley KJ (2007) A decline in mortality from prostate cancer in the UK Atomic Energy Authority workforce. Journal of Radiation Protection, 27, 437-445 Grellier J, Atkinson, W et al (2016) Risk of lung cancer mortality in nuclear workers from internal exposure to alpha particle-emitting radionuclides. Epidemiology (in press) Future outputs are expected to focus on the health effects of inhaled or ingested radionuclides which have been little studied anywhere hitherto. Outputs will include peer-reviewed publications and a publicly available web site. In all cases data will be presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where necessary.	Studies of the MR110 cohort have influenced and are expected to influence the development of the Ionising Radiation Regulations (IRRs) which regulate the exposure of people at work and of the public. The correct regulation of doses benefits the health not only of nuclear workers, but anyone else who works with radiation, such as medical radiographers and members of the public exposed as a result of medical x-rays or radioactive discharges to the environment. The IRRs are directly based on the authoritative recommendations of organisations such as the International Commission on Radiation Protection (ICRP) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). These organisations monitor the latest research literature and will be aware of past and future publications from MR110. In particular the UNSCEAR 2006 and 2012 reports cited various publications which use MR110 data. No record level data is or will be shared with any other organisation not specified in the application. Any data shared is aggregated with small number suppressed in line with HES Analysis guide.
NUVIA LTD	NUVIA LTD	MRIS - Flagging Current Status Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Study MR110 is a long standing study of the effects of occupational radiation exposure in the nuclear industry. Much of the knowledge in this area has been gained from studies of the survivors of the two atomic bombs detonated over Japan at the end of the war. However, it is not clear how relevant this study of high doses received instantaneously is, to those exposed in an occupational context, or as members of the public, to much lower doses received over many years. This is why studies of nuclear workers have been, and continue to be, important in setting acceptable exposure levels at work and for the public in the wider environment. MR110 is a study of mortality and cancer morbidity in the past and present employees of the UK Atomic Energy Authority, the government organisation responsible for the initiation and technical and scientific development of the UK’s civil nuclear energy programme. It is among the small group of UK studies that lead the world in this area partly because of the quality of the national mortality and cancer registration systems. The study commenced in 1979, designed by a team from the London School of Hygiene and Tropical Medicine. The MR110 cohort included everyone ever employed by UKAEA from its foundation in 1946. Since then all new recruits have been added to the study, which now includes around 75,000 individuals. Of these only some 28,000 are dead, which is why continued long-term follow-up, with a corresponding increase in statistical power is so important. The study was initially funded by UKAEA and largely carried out by a team from UKAEA, but with reorganisation and privatisation in the nuclear sector responsibilities have changed and it is now mostly funded by the Nuclear Decommissioning Authority (NDA), overseen by the Public Health England (PHE), Centre for Radiation, Chemical and Environmental Hazards, and carried out by Nuvia Limited. The NDA is an executive non-departmental public body, sponsored by the Department for Business, Energy & Industrial Strategy to ensure the safe and efficient clean-up of the UK’s nuclear legacy (https://www.gov.uk/government/organisations/nuclear-decommissioning-authority) The follow-up data provided by NHS Digital for the MR110 study has been used and will be used in wider collaborative studies taking in the nuclear workforces of several European countries. Such data is, of course, shared in anonymised form with collaborators and only with explicit agreement from the data providers, including NHS Digital, and from workforce representatives. Where data to be shared is anything other than data aggregated with small numbers suppressed the study will seek explicit permission from NHS Digital on a case by case basis via an amendment to the application.	The data requested and already disseminated will be accessed and processed by substantive employees of Nuvia and only for the purposes described in the application. MR110 is a flagging study, so data is provided to NHS Digital by Nuvia to enable the flags to be set. For each individual in the cohort this data comprises, name, date of birth, NHS number, home address, date to address and a unique study identifier. The data requested by Nuvia from NHS Digital falls into three categories: 1. Mortality Data Date, causes and place of death, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number, occupation, home address. 2. Emigrations and Reregistration Date of event, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number. 3. Cancer Registrations Date of diagnosis, disease code, unique study identifier, but no identity data. The study has a favourable ethical opinion from the NHS Research Ethics Committee, Oxford C. It has approval from, and reports to the PHE/NDA Epidemiology Governance Group, which includes employee representatives. Nuvia will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuvia for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide. Nuvia is currently part of a European consortium and is seeking funding to conduct a new study, which would entail data sharing with PHE. Any instances of data sharing or processing of data relating to a new study not outlined in this agreement will be subject to separate applications to NHS Digital. New data subjects are recognised and added to the SHIELD database when their personnel data is sent to the Health Effects team by UKAEA’s contractors CSC, or by DSRL and Magnox. Annually, radiation dose data arrives from the dosimetry services (ADSs) of the same employers and is linked to the data in SHIELD by name, DOB and National Insurance Number (NIN). Periodically the required details for flagging are sent to NHS Digital and Dumfries. When data comes to the study from NHS Digital, either as event notifications or members and postings listings, it is initially linked to the data in the database using name, DOB and NHS Number. Subsequently, if the study are happy that the correct person has been flagged then the study will link on member number. The study have to retain the identity data in the database even after the routine linkage has taken place, because periodically the study add new categories of exposure data. A recent example is the addition of 70 years’ worth of internal radiation contamination assessments to the SHIELD database. SHIELD is an Oracle 12 database, hosted on a server which is only accessible to authorised SHIELD users. Within SHIELD the followup data from NHS Digital is only available to those with the accredited researcher approvals. Data security is an important part of the culture of SHIELD users. Typical analyses would be: • Calculation of Standardised Mortality Ratios (SMRs) and Registration Ratios (SRRs) to compare the cohort with the national population in terms of mortality and cancer morbidity. • Calculation of Rate Ratios to compare radiation workers with non-radiation workers in the nuclear industry. • Tests for trends of mortality and morbidity rates with increasing radiation dose. • Logistic regression analyses to calculate the Excess Relative Risk per unit of radiation dose. ONS Terms and Conditions will be adhered to.	Outputs were presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where appropriate. As part of a previous EU funded project, the SHIELD database has been updated with all the internal dosimetry data required to undertake this work. The outputs included peer-reviewed publications and a publicly available web site. Data from the MR110 cohort has been used in some 20 publications in high-impact peer-reviewed journals. For example: Beral V, Inskip H, Fraser P, Booth M, Coleman D and Rose G (1985) Mortality of employees of the United Kingdom Atomic Energy Authority, 1946-1979. British Medical Journal, 291, 440-447. Fraser, P, Carpenter L, Maconochie N, Higgins C, Booth M and Beral V (1993) Cancer Mortality and morbidity in employees of the United Kingdom Atomic Energy Authority, 1946-86. British Journal of Cancer, 67, 615-624. Rooney C, Beral V, Maconochie N, Fraser P and Davies G (1993) Case-control study of prostatic cancer in employees of the United Kingdom Atomic Energy Authority. British Medical Journal, 307, 1391-1397 Roman E, Doyle P, Maconochie N, Davies G, Smith P and Beral V (1999) Cancer in children of nuclear industry employees: report on children aged under 25 years from nuclear industry family study, British Medical Journal; 318, 1443–1450 Atkinson WD, Law DV, Bromley KJ and Inskip HM (2004) Mortality of employees of the United Kingdom Atomic Energy Authority 1946-97 Occupational & .Environmental Medicine 61, 577-585 Atkinson WD, Law DV, Bromley KJ (2007) A decline in mortality from prostate cancer in the UK Atomic Energy Authority workforce. Journal of Radiation Protection, 27, 437-445 Grellier J, Atkinson, W et al (2016) Risk of lung cancer mortality in nuclear workers from internal exposure to alpha particle-emitting radionuclides. Epidemiology (in press) Future outputs are expected to focus on the health effects of inhaled or ingested radionuclides which have been little studied anywhere hitherto. Outputs will include peer-reviewed publications and a publicly available web site. In all cases data will be presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where necessary.	Studies of the MR110 cohort have influenced and are expected to influence the development of the Ionising Radiation Regulations (IRRs) which regulate the exposure of people at work and of the public. The correct regulation of doses benefits the health not only of nuclear workers, but anyone else who works with radiation, such as medical radiographers and members of the public exposed as a result of medical x-rays or radioactive discharges to the environment. The IRRs are directly based on the authoritative recommendations of organisations such as the International Commission on Radiation Protection (ICRP) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). These organisations monitor the latest research literature and will be aware of past and future publications from MR110. In particular the UNSCEAR 2006 and 2012 reports cited various publications which use MR110 data. No record level data is or will be shared with any other organisation not specified in the application. Any data shared is aggregated with small number suppressed in line with HES Analysis guide.
NUVIA LTD	NUVIA LTD	MRIS - Members and Postings Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Study MR110 is a long standing study of the effects of occupational radiation exposure in the nuclear industry. Much of the knowledge in this area has been gained from studies of the survivors of the two atomic bombs detonated over Japan at the end of the war. However, it is not clear how relevant this study of high doses received instantaneously is, to those exposed in an occupational context, or as members of the public, to much lower doses received over many years. This is why studies of nuclear workers have been, and continue to be, important in setting acceptable exposure levels at work and for the public in the wider environment. MR110 is a study of mortality and cancer morbidity in the past and present employees of the UK Atomic Energy Authority, the government organisation responsible for the initiation and technical and scientific development of the UK’s civil nuclear energy programme. It is among the small group of UK studies that lead the world in this area partly because of the quality of the national mortality and cancer registration systems. The study commenced in 1979, designed by a team from the London School of Hygiene and Tropical Medicine. The MR110 cohort included everyone ever employed by UKAEA from its foundation in 1946. Since then all new recruits have been added to the study, which now includes around 75,000 individuals. Of these only some 28,000 are dead, which is why continued long-term follow-up, with a corresponding increase in statistical power is so important. The study was initially funded by UKAEA and largely carried out by a team from UKAEA, but with reorganisation and privatisation in the nuclear sector responsibilities have changed and it is now mostly funded by the Nuclear Decommissioning Authority (NDA), overseen by the Public Health England (PHE), Centre for Radiation, Chemical and Environmental Hazards, and carried out by Nuvia Limited. The NDA is an executive non-departmental public body, sponsored by the Department for Business, Energy & Industrial Strategy to ensure the safe and efficient clean-up of the UK’s nuclear legacy (https://www.gov.uk/government/organisations/nuclear-decommissioning-authority) The follow-up data provided by NHS Digital for the MR110 study has been used and will be used in wider collaborative studies taking in the nuclear workforces of several European countries. Such data is, of course, shared in anonymised form with collaborators and only with explicit agreement from the data providers, including NHS Digital, and from workforce representatives. Where data to be shared is anything other than data aggregated with small numbers suppressed the study will seek explicit permission from NHS Digital on a case by case basis via an amendment to the application.	The data requested and already disseminated will be accessed and processed by substantive employees of Nuvia and only for the purposes described in the application. MR110 is a flagging study, so data is provided to NHS Digital by Nuvia to enable the flags to be set. For each individual in the cohort this data comprises, name, date of birth, NHS number, home address, date to address and a unique study identifier. The data requested by Nuvia from NHS Digital falls into three categories: 1. Mortality Data Date, causes and place of death, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number, occupation, home address. 2. Emigrations and Reregistration Date of event, unique study identifier, together with sufficient identity data to confirm that the flag was correctly set, i.e. name, date of birth, NHS number. 3. Cancer Registrations Date of diagnosis, disease code, unique study identifier, but no identity data. The study has a favourable ethical opinion from the NHS Research Ethics Committee, Oxford C. It has approval from, and reports to the PHE/NDA Epidemiology Governance Group, which includes employee representatives. Nuvia will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuvia for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide. Nuvia is currently part of a European consortium and is seeking funding to conduct a new study, which would entail data sharing with PHE. Any instances of data sharing or processing of data relating to a new study not outlined in this agreement will be subject to separate applications to NHS Digital. New data subjects are recognised and added to the SHIELD database when their personnel data is sent to the Health Effects team by UKAEA’s contractors CSC, or by DSRL and Magnox. Annually, radiation dose data arrives from the dosimetry services (ADSs) of the same employers and is linked to the data in SHIELD by name, DOB and National Insurance Number (NIN). Periodically the required details for flagging are sent to NHS Digital and Dumfries. When data comes to the study from NHS Digital, either as event notifications or members and postings listings, it is initially linked to the data in the database using name, DOB and NHS Number. Subsequently, if the study are happy that the correct person has been flagged then the study will link on member number. The study have to retain the identity data in the database even after the routine linkage has taken place, because periodically the study add new categories of exposure data. A recent example is the addition of 70 years’ worth of internal radiation contamination assessments to the SHIELD database. SHIELD is an Oracle 12 database, hosted on a server which is only accessible to authorised SHIELD users. Within SHIELD the followup data from NHS Digital is only available to those with the accredited researcher approvals. Data security is an important part of the culture of SHIELD users. Typical analyses would be: • Calculation of Standardised Mortality Ratios (SMRs) and Registration Ratios (SRRs) to compare the cohort with the national population in terms of mortality and cancer morbidity. • Calculation of Rate Ratios to compare radiation workers with non-radiation workers in the nuclear industry. • Tests for trends of mortality and morbidity rates with increasing radiation dose. • Logistic regression analyses to calculate the Excess Relative Risk per unit of radiation dose. ONS Terms and Conditions will be adhered to.	Outputs were presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where appropriate. As part of a previous EU funded project, the SHIELD database has been updated with all the internal dosimetry data required to undertake this work. The outputs included peer-reviewed publications and a publicly available web site. Data from the MR110 cohort has been used in some 20 publications in high-impact peer-reviewed journals. For example: Beral V, Inskip H, Fraser P, Booth M, Coleman D and Rose G (1985) Mortality of employees of the United Kingdom Atomic Energy Authority, 1946-1979. British Medical Journal, 291, 440-447. Fraser, P, Carpenter L, Maconochie N, Higgins C, Booth M and Beral V (1993) Cancer Mortality and morbidity in employees of the United Kingdom Atomic Energy Authority, 1946-86. British Journal of Cancer, 67, 615-624. Rooney C, Beral V, Maconochie N, Fraser P and Davies G (1993) Case-control study of prostatic cancer in employees of the United Kingdom Atomic Energy Authority. British Medical Journal, 307, 1391-1397 Roman E, Doyle P, Maconochie N, Davies G, Smith P and Beral V (1999) Cancer in children of nuclear industry employees: report on children aged under 25 years from nuclear industry family study, British Medical Journal; 318, 1443–1450 Atkinson WD, Law DV, Bromley KJ and Inskip HM (2004) Mortality of employees of the United Kingdom Atomic Energy Authority 1946-97 Occupational & .Environmental Medicine 61, 577-585 Atkinson WD, Law DV, Bromley KJ (2007) A decline in mortality from prostate cancer in the UK Atomic Energy Authority workforce. Journal of Radiation Protection, 27, 437-445 Grellier J, Atkinson, W et al (2016) Risk of lung cancer mortality in nuclear workers from internal exposure to alpha particle-emitting radionuclides. Epidemiology (in press) Future outputs are expected to focus on the health effects of inhaled or ingested radionuclides which have been little studied anywhere hitherto. Outputs will include peer-reviewed publications and a publicly available web site. In all cases data will be presented as aggregated tables and figures, with suppression of low-numbered cells in line with the HES analysis guide where necessary.	Studies of the MR110 cohort have influenced and are expected to influence the development of the Ionising Radiation Regulations (IRRs) which regulate the exposure of people at work and of the public. The correct regulation of doses benefits the health not only of nuclear workers, but anyone else who works with radiation, such as medical radiographers and members of the public exposed as a result of medical x-rays or radioactive discharges to the environment. The IRRs are directly based on the authoritative recommendations of organisations such as the International Commission on Radiation Protection (ICRP) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). These organisations monitor the latest research literature and will be aware of past and future publications from MR110. In particular the UNSCEAR 2006 and 2012 reports cited various publications which use MR110 data. No record level data is or will be shared with any other organisation not specified in the application. Any data shared is aggregated with small number suppressed in line with HES Analysis guide.
OFFICE FOR NATIONAL STATISTICS (ONS)	OFFICE FOR NATIONAL STATISTICS (ONS)	MRIS - Bespoke	Identifiable	Sensitive	Health and Social Care Act 2012	Ongoing	N	The Longitudinal Study (LS) contains data on 1 per cent of the population of England and Wales. It is used for several types of analysis: for example, studies using registration event data as outcomes or studies using linked census data. The purpose of these studies include those that link social, occupational and demographic information to data on vital events. Examples include studies of mortality, cancer incidence and survival, and fertility patterns. Those looking at environmental effects on health and inequalities in health. Also those investigating social mobility and the study of ageing.
OFFICE FOR NATIONAL STATISTICS (ONS)	OFFICE FOR NATIONAL STATISTICS (ONS)	MRIS - Bespoke	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Provide an assessment of the quality of the informal date of death contained within PDS death notifications, compared with formal date of death. Provide an indication as to whether the informal date of death is sufficiently accurate, and whether there would be benefit in providing this date to researchers in advance of the formal notification. To establish what advantages of timeliness this provision could bring, and whether there is any variation by age, gender and cause of death.	Compare PDS death notifications with GRO death registrations. NHS Digital will extract identifiable data (NHS Number, gender, date of birth, date of registration and ICD 10 primary cause of death code from ONS Mortality data) for persons with a date of death recorded between 1st Jan 2011 and 31th June 2016. NHS Digital will then link these data, using NHS Number, to data from Patient Demographic Service (PDS). For linked records NHS Digital will extract informal date of death and the PDS-system notification date, the formal date of death and ONS date of registration. NHS Digital will calculate age at formal date of death (using informal date if formal date not available) and stratify to the following age bands: < 5 years; 5-14 years; 15-44 years; 45-64 years; 65-74 years; 75-84 years; 85+ years. NHS Digital will calculate A. the difference between the ONS date of death and the informal date of death from PDS [ONS-PDS] and stratify as follows: zero days, 1-7 days; 8-14 days; 15-28 days; 29-90 days; 91-182 days; 183-365 days; 366-730 days; 731+ days; no ONS death recorded; no PDS death recorded. NHS Digital will calculate B. the difference between ONS date of registration and the PDS informal death system notification date [ONS registration-PDS notification], and stratify as follows: zero days, 1-7 days; 8-14 days; 15-28 days; 29-90 days; 91-182 days; 183-365 days; 366-730 days; 731+ days, no ONS death recorded; no PDS death recorded. The above difference-records (A and B separately) will be aggregated and tabulated by i) sex, ii) ICD-10 chapter, iii) age group and iv) year of death (from formal death date); and by all pairs of i) to iv). The difference-records (A and B) will also be cross-tabulated, separately for each co-variate level of the following four covariates: i) sex, ii) ICD-10 chapter, iii) age group and iv) year of death (from formal death date) Tabulations will compare the difference between the formal date of death (fDoD) and the informal date of death (iDoD), indicating the quality/accuracy of the informal death date; they will also compare the difference between the date of registration and date the informal death was recorded in PDS, indicating the days gained in advance notification using the informal date vs the formal date. In addition, we’d like to know, for each calendar year [2011 to 2015], how many iDODs were notified in that calendar year for whom there was no fDOD prior to 1 January 2016 had been notified by 30 June 2016. We’d like these counts, if possible, to be provided separately for each covariate-level of the following two covariates i) gender and iii) informal age group where age-group at death is based on iDOD (since, for these cases, fDOD has not been registered). Moreover, C. we’d like the death registration delay to be computed as [31 December 2015 – iDOD] and stratified as follows: zero days, 1-7 days; 8-14 days; 15-28 days; 29-90 days; 91-182 days; 183-365 days; 366-730 days; 731-1096 days; 1097-1462; 1463-1827. The difference-records [C.] will be aggregated and tabulated by i) sex, iii) age group and iv) year of iDOD; and by all pairs of i) to iv). Tabulated outputs, with small number suppression applied, will be provided to ONS. No record level or identifiable data will be released by NHS Digital.	The outputs will inform an assessment as to the potential advantages of using informal date of death to notify research studies of deaths in their study cohorts. At present, research-teams need to delay their record-linkage requests [for follow-up to 31 December 2015, say] by at least two years to be almost sure that ONS has been notified of almost all deaths that actually occurred in England and Wales on or before 31 December 2015. If iDoD is substantially accurate, this undesirable delay to record-linkage studies could be avoided if informal date of death was available to researchers. The research team will not know ICD-10 chapter for cause of death but in many studies, only fact-of-death was needed and, in others, imputation for likely cause-of-death may be technically possible. These are huge advantages for the discovery potential from approved record-linkage studies but are dependent on knowing how reliable iDOD is likely to be. Among those for whom iDoD exists in 2011 but no fDoD was notified by 30 June 2016, there may be falsely assigned notifications (eg in terms of NHS number) so that the number (%) of C-differences which exceed 4-years provides an upper limit for this error-rate.	Expected measurable benefits to health and/or social care including target date: More timely notification of deaths to medical researchers will benefit the health and social care system by substantially reducing delays in the discovery-potential from record-linkage studies. Currently, research-teams may observe that a subject’s series of court appearances or benefits claims has ceased but cannot know assuredly - due to lateness of notification of fDoD - whether the explanation is that the subject has died or that s/he has been rehabilitated/employed. The research team has to allow about 2-years to account for late registered deaths, which delays deriving new knowledge, in this example about criminal sanctions or benefits.
OLIVER WYMAN	OLIVER WYMAN	Bespoke Extract : SUS PbR A&E	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Oliver Wyman Ltd is requesting this data to support the work it does in ‘New Models of Care’, an initiative brought about within the NHS by the Five Year Forward View (“5YFV”). The 5YFV presented a unified call to action and laid out the strategy for the NHS to address a significant financial shortfall under the base case scenario, to be addressed by a mixture of efficiency gains, transformative new models of care and new investment in front line NHS services Four major areas of change were identified, with new models of care placed at the heart of the transformation: 1. New models of care, coupled with an increase in investment in the workforce, technology, and innovation 2. The root causes of ill health needs to be tackled 3. Patients must have control over their care 4. Action needs to be taken to meet the needs of an aging population NHS England’s recent publication on New Care Models remarked “Through the new care models programme, complete redesign of whole health and care systems are being considered. This means fewer trips to hospitals with cancer and dementia specialists holding clinics in local surgeries, having one point of call for family doctors, community nurses, social and mental health services, or access to blood tests, dialysis or even chemotherapy closer to home.” The aim of these transformative clinical models is to address the triple aim of improved clinical outcomes and enhanced patient experience at lower cost than today. Oliver Wyman requires non-identifiable non-sensitive data from NHS Digital to create the analysis to support and develop these new models of care for clients in the NHS to deliver the improvements and efficiency savings required. Oliver Wymans New Models of Care Analytics (“NMoCA”) work supports a wide spectrum of clients on the topic of New Care Models and their transformation from legacy ways of working to new models of clinical and operational delivery. Oliver Wyman currently has 5 clients (and 5 prospective) clients for which they will be working on the New Models of Care. These include Foundation Trusts, NHS England South and CCGs . In the last few years Oliver Wyman has worked with over 30 CCGs (predominantly Vanguard and/or Pioneer) and around 10 Trusts, as well as NHS England and No 10 Downing Street and the Cabinet Office. Oliver Wyman supports these clients in this work by providing reports, benchmarking, case studies, business cases, and specific analysis. All of Oliver Wyman’s current (and previous) projects seek to address a similar set of key questions – namely ‘how can we better understand the burden of need and healthcare costs across our population?’. A fundamental building block that informs the responses to this question is ‘how can we develop new models of care to serve these patients better to achieve higher quality care at lower cost?’ To benchmark the analyses of e.g. the key performance indicators of population health management (e.g. non-elective admissions, or A&E attendances, per 100,000) and the makeup of the different population segments, it has been important to use a national dataset to compare and contrast across CCGs. These outputs have highlighted areas of clear strength in some regions, and priority areas for immediate improvement elsewhere. Two datasets that are critically important to this effort are the PbR and Mental Health datasets. PbR enables Oliver Wyman to translate from core hospital activity in terms of admissions and bed days to the financial implications for the hospital and CCGs. This economic measurement is fundamental in terms of understanding the cost of care to commissioners, and therefore the value at stake if alternative approaches could reduce that activity. Using these data as a starting point is critical to understand whether investing in a new care model (e.g. an innovative primary care service) has the potential to reduce the total cost of care incurred by the system – a critical aim of the Five Year Forward View. The mental health dataset enables a better understanding of the clinical needs (both physical and mental) of specific patient cohorts. For instance, a cohort of patients with a specific combination of mental and physical diseases (e.g. dementia + another long-term condition) that compounds their usage of healthcare resources and therefore demands a mental-health centric care model rather than a physical disease-led model. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team.	Oliver Wyman’s support for Trusts, CCGs and other organisations in the health and social care sector is varied and often incorporates a wide range of data provided (including the full suite of data archetypes, e.g. demographics, provider sites, payers, diagnoses, procedures undertaken, tariffs etc.). These data enable Oliver Wyman to support their clients in better understanding their patients (e.g. their long-term conditions) and opportunities to improve care delivery (e.g. first to follow-up ratios). Equally, national ambitions to drive greater population health management at an area level means that developing a deeper understanding of the whole population of a CCG/ STP will be critical. The data will be processed by Oliver Wyman (in the location specified in this document) and uploaded raw to a secure SQL server in the location listed in this agreement. Oliver Wyman will upload the data to its SQL server, then generate several master tables combining information from across multiple years to develop a patient-centric view of activity. E.g. Oliver Wyman will create several tables that combines all the e.g. inpatient activity for each pseudonymised patient for the year. Oliver Wyman would replicate this for other settings (e.g. Outpatient, A&E) and then integrate these base tables into a consolidated master table. This could then be aggregated to produce a local health economy specific table of activity and cost. Oliver Wyman will restrict access to the database containing NHS Digital data to only those substantive employees of Oliver Wyman (based in England and Wales) and only for the purposes described in this document. These employees will have had the appropriate training and a legitimate requirement to use the data. Sysadmins on the server will not be part of the access group, and will be instructed by policy not to grant themselves access to this data. This was discussed and agreed by the NHS Digital Security Consultant, during a discussion with the Oliver Wyman data security team on 5th August 2016. Oliver Wyman will not link this data to any other record level datasets and no attempt will be made to re-identify the data. Access will be via a secure SQL server connection. No data processing will take place outside of England and Wales. All outputs would adhere to guidance on small numbers suppression in line with the HES analysis guide. Only high-level analytical outputs (never the raw data themselves) may be shared with 3rd parties Data will not be processed or accessed by any third party, and only held and processed at the addresses as per this document.	In summary outputs will be - 1.) Reports showing potential benefits of implementing New Models of Care for a specific Local Health Economy (Audience: CCG, local trusts), to include both reductions in volumes of activity (e.g. non elective admissions) as well as the economic implications for hospitals and commissioners 2.) Fact based document profiling cohorts of populations currently being poorly served by existing healthcare system (e.g. those with repeated A&E, acute and Mental Health interventions, year after year), in particular appraising their activity and cost profiles; 3.) Business Cases showing case for change for investment in New Models of Care and expected outcomes; 4.) Specific analysis supporting implementation and roll out, such as showing which long term conditions have the highest burden on the population and therefore should be focus of new proactive care programmes. Presented to CCGs, local Trusts and other interested NHS stakeholders within the Local Health Economies selected. For instance, Oliver Wyman may see a cohort of patients with a combination of mental and physical health disorders, that compounds their usage of healthcare resources and therefore demands a tailored care model that reflects the mental health needs of the cohort. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team. An example of the type of analysis undertaken has included assessing the HES admitted patient data and the accompanying PbR dataset, in conjunction with CCG population lists, to estimate the rate of non-elective admissions (and healthcare cost) per 100,000 people. In mature population health management systems this is used as a proxy for the effectiveness of the care models deployed. Oliver Wyman has completed this for organisations ranging from NHS England to local Trusts and CCGs (e.g. Blackpool CCG, Fylde and Wyre CCG, Sunderland CCG etc.) NMoCA include analyses for various local health economies, e.g. Blackpool CCG, Fylde and Wyre CCG, Oxford CCG, Sunderland CCG, Somerset CCG etc. to segment the population according to the long-term physical and mental conditions of the full population. The analyses have reviewed the relationships between the long-term conditions of the population, and the cost of care (across admitted, outpatient and A&E activity) to identify meaningful segments of people for whom current care delivery is high cost and has the potential for higher quality, with fewer avoidable interventions. These analyses may identify distinct cohorts of patients with physical conditions that could be better managed out of the hospital (e.g. in primary care and in the home). NMoCA work combines these profiles of the current system with the potential for improvement, based on best-in-class international models. The outputs of this work have ranged from estimates of the potential care cost reductions through delivering more coordinated, accountable clinical models (~5%-10% cost savings for most LHEs), to helping teams to create detailed implementation plans, and then supporting the launch of these models. Evidence from other systems suggests that a range of non-elective admissions (some disease related, others broader) have the potential to be avoided through more proactive, coordinated care. At the core of the work is an understanding of both healthcare usage (e.g. admissions, attendances and bed days) but also the cost of care that the PbR datasets will provide. Oliver Wyman has undertaken this for several CCGs, including many of those listed above. These outputs will continue to maintain a programme of work in support of NHS England and NHS Improvement to support roll out of these New Models of Care. This programme has the following specific elements: -Support the case for and delivery of the New Models of Care in the areas identified, and others beyond. Key activities will be: 1.) Demonstration of existing health needs and pressures within the local health economy (specifically in secondary and mental health care), and in comparison to other relevant local health economies, both today and over time; 2.) Identification and description of high need segments of the population across secondary and mental health, both in LHEs and nationally 3.) Estimation of impact on economics of the LHE by implementing New Models of Care targeted at high need segments; 4.) Assessment of likely impact on hospital activities and therefore (PbR-driven) economics of both hospital and wider local health economy As each Local Health economy is at different stages of development and operating in very different local environments, the approach is customized to each area. An example of current work is in the field of cancer. Oliver Wyman are working with South West Strategic Clinical Network and South West Alliance Group on opportunities to enhance cancer care. At the core of this work is developing a deeper understanding of the cost of cancer for a range of tumours, as a starting point for broader benchmarking and a debate on activities that could improve quality of care and cancer outcomes without incurring more total cost. This project is aiming to deliver new initiatives on the ground before the end of 2017.	Simon Stevens, Chief Executive of NHS England, described the Vanguard programme as aiming to: “help promote the health and well-being of the populations they serve, increasing the quality and person-centredness of care, and improve efficiency for the taxpayer within available resources”. The Director of the New Care Models programme, Sam Jones, reinforced this message: “The Five Year Forward View set out the need to do things differently across the NHS to continue to provide world-class care for patients in a clinically and financially sustainable way – pioneering new models of care is key to realising that ambition.”. The work described in this agreement is directly in support of the NHS organisations delivering these aims. Oliver Wyman are working with selected Vanguard sites and STPs to integrate care across traditional organisational boundaries, designing and delivering new, coordinated models of care that enhance the patient experience, deliver integrated high quality care and therefore avoid unnecessary care. The following provides a examples of these benefits; on the Fylde Coast, Oliver Wyman in 2014/15 worked with the local CCGs and Trust to co-develop a clinical and operational blueprint of a new model of care for those with multiple long-term conditions. This model has since been implemented with the opening of a new clinic. This new model is improving the patient experience and making cost efficiencies. This support is also integral to the development of new models of care in Somerset. For example, this work has supported design and roll-out of a new model of primary care in practices that cover a significant proportion of the CCG population. This has expanded on proof of concept analyses from HES to stratify their registered lists and deliver proactive and team-based models of care for those most at risk (e.g. leveraging analysis that reviews the relationships between long-term conditions and total secondary care cost of care). These analyses have supported the clinical and economic rationale for the role creation, recruitment and training of ‘health coaches’ to support proactive engagement with those in most need of higher frequency engagement.
OLIVER WYMAN	OLIVER WYMAN	Bespoke Extract : SUS PbR APC Episodes	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Oliver Wyman Ltd is requesting this data to support the work it does in ‘New Models of Care’, an initiative brought about within the NHS by the Five Year Forward View (“5YFV”). The 5YFV presented a unified call to action and laid out the strategy for the NHS to address a significant financial shortfall under the base case scenario, to be addressed by a mixture of efficiency gains, transformative new models of care and new investment in front line NHS services Four major areas of change were identified, with new models of care placed at the heart of the transformation: 1. New models of care, coupled with an increase in investment in the workforce, technology, and innovation 2. The root causes of ill health needs to be tackled 3. Patients must have control over their care 4. Action needs to be taken to meet the needs of an aging population NHS England’s recent publication on New Care Models remarked “Through the new care models programme, complete redesign of whole health and care systems are being considered. This means fewer trips to hospitals with cancer and dementia specialists holding clinics in local surgeries, having one point of call for family doctors, community nurses, social and mental health services, or access to blood tests, dialysis or even chemotherapy closer to home.” The aim of these transformative clinical models is to address the triple aim of improved clinical outcomes and enhanced patient experience at lower cost than today. Oliver Wyman requires non-identifiable non-sensitive data from NHS Digital to create the analysis to support and develop these new models of care for clients in the NHS to deliver the improvements and efficiency savings required. Oliver Wymans New Models of Care Analytics (“NMoCA”) work supports a wide spectrum of clients on the topic of New Care Models and their transformation from legacy ways of working to new models of clinical and operational delivery. Oliver Wyman currently has 5 clients (and 5 prospective) clients for which they will be working on the New Models of Care. These include Foundation Trusts, NHS England South and CCGs . In the last few years Oliver Wyman has worked with over 30 CCGs (predominantly Vanguard and/or Pioneer) and around 10 Trusts, as well as NHS England and No 10 Downing Street and the Cabinet Office. Oliver Wyman supports these clients in this work by providing reports, benchmarking, case studies, business cases, and specific analysis. All of Oliver Wyman’s current (and previous) projects seek to address a similar set of key questions – namely ‘how can we better understand the burden of need and healthcare costs across our population?’. A fundamental building block that informs the responses to this question is ‘how can we develop new models of care to serve these patients better to achieve higher quality care at lower cost?’ To benchmark the analyses of e.g. the key performance indicators of population health management (e.g. non-elective admissions, or A&E attendances, per 100,000) and the makeup of the different population segments, it has been important to use a national dataset to compare and contrast across CCGs. These outputs have highlighted areas of clear strength in some regions, and priority areas for immediate improvement elsewhere. Two datasets that are critically important to this effort are the PbR and Mental Health datasets. PbR enables Oliver Wyman to translate from core hospital activity in terms of admissions and bed days to the financial implications for the hospital and CCGs. This economic measurement is fundamental in terms of understanding the cost of care to commissioners, and therefore the value at stake if alternative approaches could reduce that activity. Using these data as a starting point is critical to understand whether investing in a new care model (e.g. an innovative primary care service) has the potential to reduce the total cost of care incurred by the system – a critical aim of the Five Year Forward View. The mental health dataset enables a better understanding of the clinical needs (both physical and mental) of specific patient cohorts. For instance, a cohort of patients with a specific combination of mental and physical diseases (e.g. dementia + another long-term condition) that compounds their usage of healthcare resources and therefore demands a mental-health centric care model rather than a physical disease-led model. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team.	Oliver Wyman’s support for Trusts, CCGs and other organisations in the health and social care sector is varied and often incorporates a wide range of data provided (including the full suite of data archetypes, e.g. demographics, provider sites, payers, diagnoses, procedures undertaken, tariffs etc.). These data enable Oliver Wyman to support their clients in better understanding their patients (e.g. their long-term conditions) and opportunities to improve care delivery (e.g. first to follow-up ratios). Equally, national ambitions to drive greater population health management at an area level means that developing a deeper understanding of the whole population of a CCG/ STP will be critical. The data will be processed by Oliver Wyman (in the location specified in this document) and uploaded raw to a secure SQL server in the location listed in this agreement. Oliver Wyman will upload the data to its SQL server, then generate several master tables combining information from across multiple years to develop a patient-centric view of activity. E.g. Oliver Wyman will create several tables that combines all the e.g. inpatient activity for each pseudonymised patient for the year. Oliver Wyman would replicate this for other settings (e.g. Outpatient, A&E) and then integrate these base tables into a consolidated master table. This could then be aggregated to produce a local health economy specific table of activity and cost. Oliver Wyman will restrict access to the database containing NHS Digital data to only those substantive employees of Oliver Wyman (based in England and Wales) and only for the purposes described in this document. These employees will have had the appropriate training and a legitimate requirement to use the data. Sysadmins on the server will not be part of the access group, and will be instructed by policy not to grant themselves access to this data. This was discussed and agreed by the NHS Digital Security Consultant, during a discussion with the Oliver Wyman data security team on 5th August 2016. Oliver Wyman will not link this data to any other record level datasets and no attempt will be made to re-identify the data. Access will be via a secure SQL server connection. No data processing will take place outside of England and Wales. All outputs would adhere to guidance on small numbers suppression in line with the HES analysis guide. Only high-level analytical outputs (never the raw data themselves) may be shared with 3rd parties Data will not be processed or accessed by any third party, and only held and processed at the addresses as per this document.	In summary outputs will be - 1.) Reports showing potential benefits of implementing New Models of Care for a specific Local Health Economy (Audience: CCG, local trusts), to include both reductions in volumes of activity (e.g. non elective admissions) as well as the economic implications for hospitals and commissioners 2.) Fact based document profiling cohorts of populations currently being poorly served by existing healthcare system (e.g. those with repeated A&E, acute and Mental Health interventions, year after year), in particular appraising their activity and cost profiles; 3.) Business Cases showing case for change for investment in New Models of Care and expected outcomes; 4.) Specific analysis supporting implementation and roll out, such as showing which long term conditions have the highest burden on the population and therefore should be focus of new proactive care programmes. Presented to CCGs, local Trusts and other interested NHS stakeholders within the Local Health Economies selected. For instance, Oliver Wyman may see a cohort of patients with a combination of mental and physical health disorders, that compounds their usage of healthcare resources and therefore demands a tailored care model that reflects the mental health needs of the cohort. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team. An example of the type of analysis undertaken has included assessing the HES admitted patient data and the accompanying PbR dataset, in conjunction with CCG population lists, to estimate the rate of non-elective admissions (and healthcare cost) per 100,000 people. In mature population health management systems this is used as a proxy for the effectiveness of the care models deployed. Oliver Wyman has completed this for organisations ranging from NHS England to local Trusts and CCGs (e.g. Blackpool CCG, Fylde and Wyre CCG, Sunderland CCG etc.) NMoCA include analyses for various local health economies, e.g. Blackpool CCG, Fylde and Wyre CCG, Oxford CCG, Sunderland CCG, Somerset CCG etc. to segment the population according to the long-term physical and mental conditions of the full population. The analyses have reviewed the relationships between the long-term conditions of the population, and the cost of care (across admitted, outpatient and A&E activity) to identify meaningful segments of people for whom current care delivery is high cost and has the potential for higher quality, with fewer avoidable interventions. These analyses may identify distinct cohorts of patients with physical conditions that could be better managed out of the hospital (e.g. in primary care and in the home). NMoCA work combines these profiles of the current system with the potential for improvement, based on best-in-class international models. The outputs of this work have ranged from estimates of the potential care cost reductions through delivering more coordinated, accountable clinical models (~5%-10% cost savings for most LHEs), to helping teams to create detailed implementation plans, and then supporting the launch of these models. Evidence from other systems suggests that a range of non-elective admissions (some disease related, others broader) have the potential to be avoided through more proactive, coordinated care. At the core of the work is an understanding of both healthcare usage (e.g. admissions, attendances and bed days) but also the cost of care that the PbR datasets will provide. Oliver Wyman has undertaken this for several CCGs, including many of those listed above. These outputs will continue to maintain a programme of work in support of NHS England and NHS Improvement to support roll out of these New Models of Care. This programme has the following specific elements: -Support the case for and delivery of the New Models of Care in the areas identified, and others beyond. Key activities will be: 1.) Demonstration of existing health needs and pressures within the local health economy (specifically in secondary and mental health care), and in comparison to other relevant local health economies, both today and over time; 2.) Identification and description of high need segments of the population across secondary and mental health, both in LHEs and nationally 3.) Estimation of impact on economics of the LHE by implementing New Models of Care targeted at high need segments; 4.) Assessment of likely impact on hospital activities and therefore (PbR-driven) economics of both hospital and wider local health economy As each Local Health economy is at different stages of development and operating in very different local environments, the approach is customized to each area. An example of current work is in the field of cancer. Oliver Wyman are working with South West Strategic Clinical Network and South West Alliance Group on opportunities to enhance cancer care. At the core of this work is developing a deeper understanding of the cost of cancer for a range of tumours, as a starting point for broader benchmarking and a debate on activities that could improve quality of care and cancer outcomes without incurring more total cost. This project is aiming to deliver new initiatives on the ground before the end of 2017.	Simon Stevens, Chief Executive of NHS England, described the Vanguard programme as aiming to: “help promote the health and well-being of the populations they serve, increasing the quality and person-centredness of care, and improve efficiency for the taxpayer within available resources”. The Director of the New Care Models programme, Sam Jones, reinforced this message: “The Five Year Forward View set out the need to do things differently across the NHS to continue to provide world-class care for patients in a clinically and financially sustainable way – pioneering new models of care is key to realising that ambition.”. The work described in this agreement is directly in support of the NHS organisations delivering these aims. Oliver Wyman are working with selected Vanguard sites and STPs to integrate care across traditional organisational boundaries, designing and delivering new, coordinated models of care that enhance the patient experience, deliver integrated high quality care and therefore avoid unnecessary care. The following provides a examples of these benefits; on the Fylde Coast, Oliver Wyman in 2014/15 worked with the local CCGs and Trust to co-develop a clinical and operational blueprint of a new model of care for those with multiple long-term conditions. This model has since been implemented with the opening of a new clinic. This new model is improving the patient experience and making cost efficiencies. This support is also integral to the development of new models of care in Somerset. For example, this work has supported design and roll-out of a new model of primary care in practices that cover a significant proportion of the CCG population. This has expanded on proof of concept analyses from HES to stratify their registered lists and deliver proactive and team-based models of care for those most at risk (e.g. leveraging analysis that reviews the relationships between long-term conditions and total secondary care cost of care). These analyses have supported the clinical and economic rationale for the role creation, recruitment and training of ‘health coaches’ to support proactive engagement with those in most need of higher frequency engagement.
OLIVER WYMAN	OLIVER WYMAN	Bespoke Extract : SUS PbR APC Spells	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Oliver Wyman Ltd is requesting this data to support the work it does in ‘New Models of Care’, an initiative brought about within the NHS by the Five Year Forward View (“5YFV”). The 5YFV presented a unified call to action and laid out the strategy for the NHS to address a significant financial shortfall under the base case scenario, to be addressed by a mixture of efficiency gains, transformative new models of care and new investment in front line NHS services Four major areas of change were identified, with new models of care placed at the heart of the transformation: 1. New models of care, coupled with an increase in investment in the workforce, technology, and innovation 2. The root causes of ill health needs to be tackled 3. Patients must have control over their care 4. Action needs to be taken to meet the needs of an aging population NHS England’s recent publication on New Care Models remarked “Through the new care models programme, complete redesign of whole health and care systems are being considered. This means fewer trips to hospitals with cancer and dementia specialists holding clinics in local surgeries, having one point of call for family doctors, community nurses, social and mental health services, or access to blood tests, dialysis or even chemotherapy closer to home.” The aim of these transformative clinical models is to address the triple aim of improved clinical outcomes and enhanced patient experience at lower cost than today. Oliver Wyman requires non-identifiable non-sensitive data from NHS Digital to create the analysis to support and develop these new models of care for clients in the NHS to deliver the improvements and efficiency savings required. Oliver Wymans New Models of Care Analytics (“NMoCA”) work supports a wide spectrum of clients on the topic of New Care Models and their transformation from legacy ways of working to new models of clinical and operational delivery. Oliver Wyman currently has 5 clients (and 5 prospective) clients for which they will be working on the New Models of Care. These include Foundation Trusts, NHS England South and CCGs . In the last few years Oliver Wyman has worked with over 30 CCGs (predominantly Vanguard and/or Pioneer) and around 10 Trusts, as well as NHS England and No 10 Downing Street and the Cabinet Office. Oliver Wyman supports these clients in this work by providing reports, benchmarking, case studies, business cases, and specific analysis. All of Oliver Wyman’s current (and previous) projects seek to address a similar set of key questions – namely ‘how can we better understand the burden of need and healthcare costs across our population?’. A fundamental building block that informs the responses to this question is ‘how can we develop new models of care to serve these patients better to achieve higher quality care at lower cost?’ To benchmark the analyses of e.g. the key performance indicators of population health management (e.g. non-elective admissions, or A&E attendances, per 100,000) and the makeup of the different population segments, it has been important to use a national dataset to compare and contrast across CCGs. These outputs have highlighted areas of clear strength in some regions, and priority areas for immediate improvement elsewhere. Two datasets that are critically important to this effort are the PbR and Mental Health datasets. PbR enables Oliver Wyman to translate from core hospital activity in terms of admissions and bed days to the financial implications for the hospital and CCGs. This economic measurement is fundamental in terms of understanding the cost of care to commissioners, and therefore the value at stake if alternative approaches could reduce that activity. Using these data as a starting point is critical to understand whether investing in a new care model (e.g. an innovative primary care service) has the potential to reduce the total cost of care incurred by the system – a critical aim of the Five Year Forward View. The mental health dataset enables a better understanding of the clinical needs (both physical and mental) of specific patient cohorts. For instance, a cohort of patients with a specific combination of mental and physical diseases (e.g. dementia + another long-term condition) that compounds their usage of healthcare resources and therefore demands a mental-health centric care model rather than a physical disease-led model. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team.	Oliver Wyman’s support for Trusts, CCGs and other organisations in the health and social care sector is varied and often incorporates a wide range of data provided (including the full suite of data archetypes, e.g. demographics, provider sites, payers, diagnoses, procedures undertaken, tariffs etc.). These data enable Oliver Wyman to support their clients in better understanding their patients (e.g. their long-term conditions) and opportunities to improve care delivery (e.g. first to follow-up ratios). Equally, national ambitions to drive greater population health management at an area level means that developing a deeper understanding of the whole population of a CCG/ STP will be critical. The data will be processed by Oliver Wyman (in the location specified in this document) and uploaded raw to a secure SQL server in the location listed in this agreement. Oliver Wyman will upload the data to its SQL server, then generate several master tables combining information from across multiple years to develop a patient-centric view of activity. E.g. Oliver Wyman will create several tables that combines all the e.g. inpatient activity for each pseudonymised patient for the year. Oliver Wyman would replicate this for other settings (e.g. Outpatient, A&E) and then integrate these base tables into a consolidated master table. This could then be aggregated to produce a local health economy specific table of activity and cost. Oliver Wyman will restrict access to the database containing NHS Digital data to only those substantive employees of Oliver Wyman (based in England and Wales) and only for the purposes described in this document. These employees will have had the appropriate training and a legitimate requirement to use the data. Sysadmins on the server will not be part of the access group, and will be instructed by policy not to grant themselves access to this data. This was discussed and agreed by the NHS Digital Security Consultant, during a discussion with the Oliver Wyman data security team on 5th August 2016. Oliver Wyman will not link this data to any other record level datasets and no attempt will be made to re-identify the data. Access will be via a secure SQL server connection. No data processing will take place outside of England and Wales. All outputs would adhere to guidance on small numbers suppression in line with the HES analysis guide. Only high-level analytical outputs (never the raw data themselves) may be shared with 3rd parties Data will not be processed or accessed by any third party, and only held and processed at the addresses as per this document.	In summary outputs will be - 1.) Reports showing potential benefits of implementing New Models of Care for a specific Local Health Economy (Audience: CCG, local trusts), to include both reductions in volumes of activity (e.g. non elective admissions) as well as the economic implications for hospitals and commissioners 2.) Fact based document profiling cohorts of populations currently being poorly served by existing healthcare system (e.g. those with repeated A&E, acute and Mental Health interventions, year after year), in particular appraising their activity and cost profiles; 3.) Business Cases showing case for change for investment in New Models of Care and expected outcomes; 4.) Specific analysis supporting implementation and roll out, such as showing which long term conditions have the highest burden on the population and therefore should be focus of new proactive care programmes. Presented to CCGs, local Trusts and other interested NHS stakeholders within the Local Health Economies selected. For instance, Oliver Wyman may see a cohort of patients with a combination of mental and physical health disorders, that compounds their usage of healthcare resources and therefore demands a tailored care model that reflects the mental health needs of the cohort. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team. An example of the type of analysis undertaken has included assessing the HES admitted patient data and the accompanying PbR dataset, in conjunction with CCG population lists, to estimate the rate of non-elective admissions (and healthcare cost) per 100,000 people. In mature population health management systems this is used as a proxy for the effectiveness of the care models deployed. Oliver Wyman has completed this for organisations ranging from NHS England to local Trusts and CCGs (e.g. Blackpool CCG, Fylde and Wyre CCG, Sunderland CCG etc.) NMoCA include analyses for various local health economies, e.g. Blackpool CCG, Fylde and Wyre CCG, Oxford CCG, Sunderland CCG, Somerset CCG etc. to segment the population according to the long-term physical and mental conditions of the full population. The analyses have reviewed the relationships between the long-term conditions of the population, and the cost of care (across admitted, outpatient and A&E activity) to identify meaningful segments of people for whom current care delivery is high cost and has the potential for higher quality, with fewer avoidable interventions. These analyses may identify distinct cohorts of patients with physical conditions that could be better managed out of the hospital (e.g. in primary care and in the home). NMoCA work combines these profiles of the current system with the potential for improvement, based on best-in-class international models. The outputs of this work have ranged from estimates of the potential care cost reductions through delivering more coordinated, accountable clinical models (~5%-10% cost savings for most LHEs), to helping teams to create detailed implementation plans, and then supporting the launch of these models. Evidence from other systems suggests that a range of non-elective admissions (some disease related, others broader) have the potential to be avoided through more proactive, coordinated care. At the core of the work is an understanding of both healthcare usage (e.g. admissions, attendances and bed days) but also the cost of care that the PbR datasets will provide. Oliver Wyman has undertaken this for several CCGs, including many of those listed above. These outputs will continue to maintain a programme of work in support of NHS England and NHS Improvement to support roll out of these New Models of Care. This programme has the following specific elements: -Support the case for and delivery of the New Models of Care in the areas identified, and others beyond. Key activities will be: 1.) Demonstration of existing health needs and pressures within the local health economy (specifically in secondary and mental health care), and in comparison to other relevant local health economies, both today and over time; 2.) Identification and description of high need segments of the population across secondary and mental health, both in LHEs and nationally 3.) Estimation of impact on economics of the LHE by implementing New Models of Care targeted at high need segments; 4.) Assessment of likely impact on hospital activities and therefore (PbR-driven) economics of both hospital and wider local health economy As each Local Health economy is at different stages of development and operating in very different local environments, the approach is customized to each area. An example of current work is in the field of cancer. Oliver Wyman are working with South West Strategic Clinical Network and South West Alliance Group on opportunities to enhance cancer care. At the core of this work is developing a deeper understanding of the cost of cancer for a range of tumours, as a starting point for broader benchmarking and a debate on activities that could improve quality of care and cancer outcomes without incurring more total cost. This project is aiming to deliver new initiatives on the ground before the end of 2017.	Simon Stevens, Chief Executive of NHS England, described the Vanguard programme as aiming to: “help promote the health and well-being of the populations they serve, increasing the quality and person-centredness of care, and improve efficiency for the taxpayer within available resources”. The Director of the New Care Models programme, Sam Jones, reinforced this message: “The Five Year Forward View set out the need to do things differently across the NHS to continue to provide world-class care for patients in a clinically and financially sustainable way – pioneering new models of care is key to realising that ambition.”. The work described in this agreement is directly in support of the NHS organisations delivering these aims. Oliver Wyman are working with selected Vanguard sites and STPs to integrate care across traditional organisational boundaries, designing and delivering new, coordinated models of care that enhance the patient experience, deliver integrated high quality care and therefore avoid unnecessary care. The following provides a examples of these benefits; on the Fylde Coast, Oliver Wyman in 2014/15 worked with the local CCGs and Trust to co-develop a clinical and operational blueprint of a new model of care for those with multiple long-term conditions. This model has since been implemented with the opening of a new clinic. This new model is improving the patient experience and making cost efficiencies. This support is also integral to the development of new models of care in Somerset. For example, this work has supported design and roll-out of a new model of primary care in practices that cover a significant proportion of the CCG population. This has expanded on proof of concept analyses from HES to stratify their registered lists and deliver proactive and team-based models of care for those most at risk (e.g. leveraging analysis that reviews the relationships between long-term conditions and total secondary care cost of care). These analyses have supported the clinical and economic rationale for the role creation, recruitment and training of ‘health coaches’ to support proactive engagement with those in most need of higher frequency engagement.
OLIVER WYMAN	OLIVER WYMAN	Bespoke Extract : SUS PbR OP	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	Oliver Wyman Ltd is requesting this data to support the work it does in ‘New Models of Care’, an initiative brought about within the NHS by the Five Year Forward View (“5YFV”). The 5YFV presented a unified call to action and laid out the strategy for the NHS to address a significant financial shortfall under the base case scenario, to be addressed by a mixture of efficiency gains, transformative new models of care and new investment in front line NHS services Four major areas of change were identified, with new models of care placed at the heart of the transformation: 1. New models of care, coupled with an increase in investment in the workforce, technology, and innovation 2. The root causes of ill health needs to be tackled 3. Patients must have control over their care 4. Action needs to be taken to meet the needs of an aging population NHS England’s recent publication on New Care Models remarked “Through the new care models programme, complete redesign of whole health and care systems are being considered. This means fewer trips to hospitals with cancer and dementia specialists holding clinics in local surgeries, having one point of call for family doctors, community nurses, social and mental health services, or access to blood tests, dialysis or even chemotherapy closer to home.” The aim of these transformative clinical models is to address the triple aim of improved clinical outcomes and enhanced patient experience at lower cost than today. Oliver Wyman requires non-identifiable non-sensitive data from NHS Digital to create the analysis to support and develop these new models of care for clients in the NHS to deliver the improvements and efficiency savings required. Oliver Wymans New Models of Care Analytics (“NMoCA”) work supports a wide spectrum of clients on the topic of New Care Models and their transformation from legacy ways of working to new models of clinical and operational delivery. Oliver Wyman currently has 5 clients (and 5 prospective) clients for which they will be working on the New Models of Care. These include Foundation Trusts, NHS England South and CCGs . In the last few years Oliver Wyman has worked with over 30 CCGs (predominantly Vanguard and/or Pioneer) and around 10 Trusts, as well as NHS England and No 10 Downing Street and the Cabinet Office. Oliver Wyman supports these clients in this work by providing reports, benchmarking, case studies, business cases, and specific analysis. All of Oliver Wyman’s current (and previous) projects seek to address a similar set of key questions – namely ‘how can we better understand the burden of need and healthcare costs across our population?’. A fundamental building block that informs the responses to this question is ‘how can we develop new models of care to serve these patients better to achieve higher quality care at lower cost?’ To benchmark the analyses of e.g. the key performance indicators of population health management (e.g. non-elective admissions, or A&E attendances, per 100,000) and the makeup of the different population segments, it has been important to use a national dataset to compare and contrast across CCGs. These outputs have highlighted areas of clear strength in some regions, and priority areas for immediate improvement elsewhere. Two datasets that are critically important to this effort are the PbR and Mental Health datasets. PbR enables Oliver Wyman to translate from core hospital activity in terms of admissions and bed days to the financial implications for the hospital and CCGs. This economic measurement is fundamental in terms of understanding the cost of care to commissioners, and therefore the value at stake if alternative approaches could reduce that activity. Using these data as a starting point is critical to understand whether investing in a new care model (e.g. an innovative primary care service) has the potential to reduce the total cost of care incurred by the system – a critical aim of the Five Year Forward View. The mental health dataset enables a better understanding of the clinical needs (both physical and mental) of specific patient cohorts. For instance, a cohort of patients with a specific combination of mental and physical diseases (e.g. dementia + another long-term condition) that compounds their usage of healthcare resources and therefore demands a mental-health centric care model rather than a physical disease-led model. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team.	Oliver Wyman’s support for Trusts, CCGs and other organisations in the health and social care sector is varied and often incorporates a wide range of data provided (including the full suite of data archetypes, e.g. demographics, provider sites, payers, diagnoses, procedures undertaken, tariffs etc.). These data enable Oliver Wyman to support their clients in better understanding their patients (e.g. their long-term conditions) and opportunities to improve care delivery (e.g. first to follow-up ratios). Equally, national ambitions to drive greater population health management at an area level means that developing a deeper understanding of the whole population of a CCG/ STP will be critical. The data will be processed by Oliver Wyman (in the location specified in this document) and uploaded raw to a secure SQL server in the location listed in this agreement. Oliver Wyman will upload the data to its SQL server, then generate several master tables combining information from across multiple years to develop a patient-centric view of activity. E.g. Oliver Wyman will create several tables that combines all the e.g. inpatient activity for each pseudonymised patient for the year. Oliver Wyman would replicate this for other settings (e.g. Outpatient, A&E) and then integrate these base tables into a consolidated master table. This could then be aggregated to produce a local health economy specific table of activity and cost. Oliver Wyman will restrict access to the database containing NHS Digital data to only those substantive employees of Oliver Wyman (based in England and Wales) and only for the purposes described in this document. These employees will have had the appropriate training and a legitimate requirement to use the data. Sysadmins on the server will not be part of the access group, and will be instructed by policy not to grant themselves access to this data. This was discussed and agreed by the NHS Digital Security Consultant, during a discussion with the Oliver Wyman data security team on 5th August 2016. Oliver Wyman will not link this data to any other record level datasets and no attempt will be made to re-identify the data. Access will be via a secure SQL server connection. No data processing will take place outside of England and Wales. All outputs would adhere to guidance on small numbers suppression in line with the HES analysis guide. Only high-level analytical outputs (never the raw data themselves) may be shared with 3rd parties Data will not be processed or accessed by any third party, and only held and processed at the addresses as per this document.	In summary outputs will be - 1.) Reports showing potential benefits of implementing New Models of Care for a specific Local Health Economy (Audience: CCG, local trusts), to include both reductions in volumes of activity (e.g. non elective admissions) as well as the economic implications for hospitals and commissioners 2.) Fact based document profiling cohorts of populations currently being poorly served by existing healthcare system (e.g. those with repeated A&E, acute and Mental Health interventions, year after year), in particular appraising their activity and cost profiles; 3.) Business Cases showing case for change for investment in New Models of Care and expected outcomes; 4.) Specific analysis supporting implementation and roll out, such as showing which long term conditions have the highest burden on the population and therefore should be focus of new proactive care programmes. Presented to CCGs, local Trusts and other interested NHS stakeholders within the Local Health Economies selected. For instance, Oliver Wyman may see a cohort of patients with a combination of mental and physical health disorders, that compounds their usage of healthcare resources and therefore demands a tailored care model that reflects the mental health needs of the cohort. These insights would inform the composition of care model teams, that for some areas would require designated mental healthcare team members as part of the integrated team. An example of the type of analysis undertaken has included assessing the HES admitted patient data and the accompanying PbR dataset, in conjunction with CCG population lists, to estimate the rate of non-elective admissions (and healthcare cost) per 100,000 people. In mature population health management systems this is used as a proxy for the effectiveness of the care models deployed. Oliver Wyman has completed this for organisations ranging from NHS England to local Trusts and CCGs (e.g. Blackpool CCG, Fylde and Wyre CCG, Sunderland CCG etc.) NMoCA include analyses for various local health economies, e.g. Blackpool CCG, Fylde and Wyre CCG, Oxford CCG, Sunderland CCG, Somerset CCG etc. to segment the population according to the long-term physical and mental conditions of the full population. The analyses have reviewed the relationships between the long-term conditions of the population, and the cost of care (across admitted, outpatient and A&E activity) to identify meaningful segments of people for whom current care delivery is high cost and has the potential for higher quality, with fewer avoidable interventions. These analyses may identify distinct cohorts of patients with physical conditions that could be better managed out of the hospital (e.g. in primary care and in the home). NMoCA work combines these profiles of the current system with the potential for improvement, based on best-in-class international models. The outputs of this work have ranged from estimates of the potential care cost reductions through delivering more coordinated, accountable clinical models (~5%-10% cost savings for most LHEs), to helping teams to create detailed implementation plans, and then supporting the launch of these models. Evidence from other systems suggests that a range of non-elective admissions (some disease related, others broader) have the potential to be avoided through more proactive, coordinated care. At the core of the work is an understanding of both healthcare usage (e.g. admissions, attendances and bed days) but also the cost of care that the PbR datasets will provide. Oliver Wyman has undertaken this for several CCGs, including many of those listed above. These outputs will continue to maintain a programme of work in support of NHS England and NHS Improvement to support roll out of these New Models of Care. This programme has the following specific elements: -Support the case for and delivery of the New Models of Care in the areas identified, and others beyond. Key activities will be: 1.) Demonstration of existing health needs and pressures within the local health economy (specifically in secondary and mental health care), and in comparison to other relevant local health economies, both today and over time; 2.) Identification and description of high need segments of the population across secondary and mental health, both in LHEs and nationally 3.) Estimation of impact on economics of the LHE by implementing New Models of Care targeted at high need segments; 4.) Assessment of likely impact on hospital activities and therefore (PbR-driven) economics of both hospital and wider local health economy As each Local Health economy is at different stages of development and operating in very different local environments, the approach is customized to each area. An example of current work is in the field of cancer. Oliver Wyman are working with South West Strategic Clinical Network and South West Alliance Group on opportunities to enhance cancer care. At the core of this work is developing a deeper understanding of the cost of cancer for a range of tumours, as a starting point for broader benchmarking and a debate on activities that could improve quality of care and cancer outcomes without incurring more total cost. This project is aiming to deliver new initiatives on the ground before the end of 2017.	Simon Stevens, Chief Executive of NHS England, described the Vanguard programme as aiming to: “help promote the health and well-being of the populations they serve, increasing the quality and person-centredness of care, and improve efficiency for the taxpayer within available resources”. The Director of the New Care Models programme, Sam Jones, reinforced this message: “The Five Year Forward View set out the need to do things differently across the NHS to continue to provide world-class care for patients in a clinically and financially sustainable way – pioneering new models of care is key to realising that ambition.”. The work described in this agreement is directly in support of the NHS organisations delivering these aims. Oliver Wyman are working with selected Vanguard sites and STPs to integrate care across traditional organisational boundaries, designing and delivering new, coordinated models of care that enhance the patient experience, deliver integrated high quality care and therefore avoid unnecessary care. The following provides a examples of these benefits; on the Fylde Coast, Oliver Wyman in 2014/15 worked with the local CCGs and Trust to co-develop a clinical and operational blueprint of a new model of care for those with multiple long-term conditions. This model has since been implemented with the opening of a new clinic. This new model is improving the patient experience and making cost efficiencies. This support is also integral to the development of new models of care in Somerset. For example, this work has supported design and roll-out of a new model of primary care in practices that cover a significant proportion of the CCG population. This has expanded on proof of concept analyses from HES to stratify their registered lists and deliver proactive and team-based models of care for those most at risk (e.g. leveraging analysis that reviews the relationships between long-term conditions and total secondary care cost of care). These analyses have supported the clinical and economic rationale for the role creation, recruitment and training of ‘health coaches’ to support proactive engagement with those in most need of higher frequency engagement.
PATHWAY COMMUNICATIONS LTD	PATHWAY COMMUNICATIONS LTD	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This application can be summarised as having two purposes: 1. Forecasting Demand for Ophthalmology Services 2. Ophthalmology Pathway Simulation The data will not be used for sales or marketing purposes. Pathway Communications customers for both purposes will be limited solely to NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs. Pathway have been working on a pilot with 3 Trusts (South Warwickshire NHS Trust, Cambridge Hospital University NHS Foundation Trust and Maidstone and Tunbridge Wells NHS Trust), but the customer base is likely to expand (forecast 15-20 Trusts by the end of 2017). 1. Forecasting Pathway Communications has developed a Forecasting methodology to assist NHS managers to better understand demand for future services with forecast accuracies in the region of 90% (other typical models produce accuracies around 70%-80%). Users of the record level data will be restricted to Pathway Communications staff only. Static forecasting reports will be generated for customer organisations (NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs) only. 2. Ophthalmology Pathway Simulation Pathway Communications build discrete event simulations of patient pathways. These simulations show all activity and resources consumed for that disease area. They enable the impact on resources of any change in services for patients, to be explored in a safe ‘virtual’ environment helping service leads to identify improvements in service provision, for the benefit of patients. This type of simulation is supported by NICE as an approved methodology for exploring patient pathways. Pathway Communications has been asked by South Warwickshire NHS Trust, Cambridge Hospital University NHS Foundation Trust and Maidstone and Tunbridge Wells NHS Trusts (but later for other customer organisations: NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs) to develop an operational simulation to fully replicate their ophthalmology service in order to: • explore the impact of making changes to the service, including the impact of introducing Saturday clinics, likely requirements for agency staff, future utilisation of beds/theatres/consultants and financial reporting (e.g. revenue vs. costs). • optimise their appointment booking system and inspect the effect of small changes. • Compare forecasts for these Trusts with all other Trusts based on key performance indicators, including 18-week target and readmissions. HES data is requested to enable Pathway Communications to develop a simulation tool for these Trusts to provide accurate information about how, the number of, and over what period of time, patients move through the service (forecasted activity). The simulation will use these numbers to calculate the resources (staff time, costs, use of equipment etc.) that are consumed at any point in time. The use of HES data will enable the development of a simulation that is a reflection of historic reality and therefore can provide a very high level of accuracy of the future so improvements to the service can be explored for the benefit of patients, and accurate prediction of future trends made, to enable better planning for service managers. Pathway Communications will only be producing forecasts of future activity based on historic HES data patterns. Users of the reports and simulations will be the Management team for Ophthalmology Services at NHS Trusts. At no point is it possible to trace the movement of individual patients, nor to link the numbers of patients to unique patient identifiers. Small numbers are automatically suppressed in accordance with HSCIC guidance. For both purposes record level HES data will be securely held, stored and processed within Pathway Communications offices only and accessed by specific Pathway Communications staff only. No record level HES data will be available to these Trusts and the HES data will not be linked to any other data. The Macular Society will have no access to the data. Pathway Communications recognises the requirements of the Data Protection Act to only hold data that is proportionate to the requirements for use. They are requesting data that relates to work that is currently supported by Maidstone and Tunbridge Wells NHS Trust , South Warwickshire NHS Trust and Cambridge Hospital University NHS Foundation Trust. Full ophthalmology data for all service providers is required however to allow comparison against all other Trusts, in relation to key metrics such as 18 week waiting time targets, 28 day emergency readmissions, did not attends and cancellations. Quarterly updates are required so that these Trusts can carry out regular comparisons of their performance against other Trusts. The risk of any re-identification will be mitigated by only including counts or estimated averages in outputs. The HSCIC HES Analysis Guide includes guidance on the suppression of small numbers, and these rules will be applied to all the simulation and forecasted outputs.	Pathway Communications will receive HES data from the HSCIC related to Ophthalmology episodes, filtered by treatment specialty 130 and all diagnosis codes returned within that treatment specialty. 1. Forecasting Demand for Ophthalmology Services Pathway Communications will look at the number of inpatient admissions (elective/non-elective), outpatient attendances, follow-ups, did not attends and cancellations. These will be counted for each month (i.e. 36 historical data points) where forecasting algorithms will be used to predict demand for services over the next 12 months. 2. Ophthalmology Pathway Simulation Pathway Communications will count the number of arrivals (and mode of arrival) for these Trusts' ophthalmology service, including individual patient’s footsteps in Inpatient and Outpatient. Additional information will be extracted, including average length of stay, average waiting time for an Outpatient appointment, average number of follow-ups and average cost of care (HRG codes). These will then become key input parameters for the simulation model (discrete event simulation using SIMUL8). Forecasted activity will also be embedded into the simulation model.	1. Forecasting Demand for Ophthalmology Services The forecasting tool will only output forecasted activity for services over the next 12 months. The forecasted activity will include inpatient admissions, outpatient attendances. The outputs from Forecasting will be restricted to static reports and small numbers not suppressed as it is a prediction and not the data itself. Pathway Communications will forecast monthly first attendances, follow-ups, DNAs and cancellations. These forecasts will not produce indicators showing performance of the Trust. Target date: the tool will be complete 3 months after HES is available 2. Ophthalmology Pathway Simulation The outputs from HES associated with “ophthalmology pathway simulation are the estimated summary statistics used as input parameters in to the simulation model (i.e. the average length of stay, average cost of the pathway using HRG codes, number of admissions/attendances/DNAs/cancellations and follow-ups). The simulation will output metrics associated with: • Staff utilization (in percentages) • Bed/theatre/clinic slot utilization (in percentages) • Diagnostic/Treatment procedures (in numbers) • Monthly waiting time (in weeks) • Monthly revenue and costs (in pounds) • Comparison of these Trusts against all other Trusts in relation to waiting time and readmissions (in weeks and numbers) These simulations will enable rapid exploration of future service capacity and demand across the Trust. The outputs from the simulation will be Powerpoint graphs and charts in pdf format. They will not contain any record level HES data, only aggregated data (with small numbers suppressed in line with the HES Analysis Guide). These simulations will produce indicators showing performance of these Trusts against other Trusts. Target date: the simulation will be complete 3 months after HES is available	Without building the simulations and forecasting tool and populating them with the HES data it is impossible to predict what specific measurable benefits these Trusts will experience. However the key purpose of the development is to enable numerous different service improvement options to be explored and understood. The forecasting and ophthalmology patient pathway simulations will both provide useful tools for forecasting future activity for these Trusts. Often such forecasts take the Trust months to prepare and are not statistically validated, resulting in inaccurate predictions. The forecasting and simulations will replace this with a few minutes’ work. As far as Pathway Communications are aware, the combination of forecasting with simulation is not available to the NHS. The forecasting will: • Enable these Trusts to better understand their demand with accuracies in the region of 90%, hence the opportunity to plan well ahead into the future with much greater reliability than before. • update these Trusts demand predictions on a quarterly basis, which will alert the management if there is an expected surge of inpatient admissions/attendances. • Uncover demand and capacity issues instantly to enable quicker reaction to changes in acute activity so that necessary measures can be in place to ensure effective delivery of care at all times to ensure continuity of care to patients. The Ophthalmology Pathway Simulation for these Trusts will: • enable the full impact of changes to these Trusts ophthalmology service to be explored in the safety of a ‘virtual’ environment. At present due to the complexity in ophthalmology services, changes are introduced without a full understanding of the impact they will have on other areas of the service. • the outputs from the simulation (the results of the impact on resources, numbers of patient treated etc.) will be able to be used to support strong business cases for change to board-level executives and holders of budgets, thus dramatically shortening the time from identifying the service improvements to be made, to implementing the improvements. Currently this can often take around a year. Using the simulation, these Trusts will be able to shorten this to a matter of a few weeks. • the simulation will be fully statistically validated so that the results can be relied on to be accurate. At present service improvements are often based on subjective opinions of what improvements should be made. Pathway Communications will require 3 months after HES data is available, to complete building the simulation and forecasts for these Trusts and expect the Trusts to start to benefit from using these tools 6-8 weeks after this.
PATHWAY COMMUNICATIONS LTD	PATHWAY COMMUNICATIONS LTD	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	This application can be summarised as having two purposes: 1. Forecasting Demand for Ophthalmology Services 2. Ophthalmology Pathway Simulation The data will not be used for sales or marketing purposes. Pathway Communications customers for both purposes will be limited solely to NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs. Pathway have been working on a pilot with 3 Trusts (South Warwickshire NHS Trust, Cambridge Hospital University NHS Foundation Trust and Maidstone and Tunbridge Wells NHS Trust), but the customer base is likely to expand (forecast 15-20 Trusts by the end of 2017). 1. Forecasting Pathway Communications has developed a Forecasting methodology to assist NHS managers to better understand demand for future services with forecast accuracies in the region of 90% (other typical models produce accuracies around 70%-80%). Users of the record level data will be restricted to Pathway Communications staff only. Static forecasting reports will be generated for customer organisations (NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs) only. 2. Ophthalmology Pathway Simulation Pathway Communications build discrete event simulations of patient pathways. These simulations show all activity and resources consumed for that disease area. They enable the impact on resources of any change in services for patients, to be explored in a safe ‘virtual’ environment helping service leads to identify improvements in service provision, for the benefit of patients. This type of simulation is supported by NICE as an approved methodology for exploring patient pathways. Pathway Communications has been asked by South Warwickshire NHS Trust, Cambridge Hospital University NHS Foundation Trust and Maidstone and Tunbridge Wells NHS Trusts (but later for other customer organisations: NHS Trusts, NHS Foundation Trusts, CCGs, and CSUs) to develop an operational simulation to fully replicate their ophthalmology service in order to: • explore the impact of making changes to the service, including the impact of introducing Saturday clinics, likely requirements for agency staff, future utilisation of beds/theatres/consultants and financial reporting (e.g. revenue vs. costs). • optimise their appointment booking system and inspect the effect of small changes. • Compare forecasts for these Trusts with all other Trusts based on key performance indicators, including 18-week target and readmissions. HES data is requested to enable Pathway Communications to develop a simulation tool for these Trusts to provide accurate information about how, the number of, and over what period of time, patients move through the service (forecasted activity). The simulation will use these numbers to calculate the resources (staff time, costs, use of equipment etc.) that are consumed at any point in time. The use of HES data will enable the development of a simulation that is a reflection of historic reality and therefore can provide a very high level of accuracy of the future so improvements to the service can be explored for the benefit of patients, and accurate prediction of future trends made, to enable better planning for service managers. Pathway Communications will only be producing forecasts of future activity based on historic HES data patterns. Users of the reports and simulations will be the Management team for Ophthalmology Services at NHS Trusts. At no point is it possible to trace the movement of individual patients, nor to link the numbers of patients to unique patient identifiers. Small numbers are automatically suppressed in accordance with HSCIC guidance. For both purposes record level HES data will be securely held, stored and processed within Pathway Communications offices only and accessed by specific Pathway Communications staff only. No record level HES data will be available to these Trusts and the HES data will not be linked to any other data. The Macular Society will have no access to the data. Pathway Communications recognises the requirements of the Data Protection Act to only hold data that is proportionate to the requirements for use. They are requesting data that relates to work that is currently supported by Maidstone and Tunbridge Wells NHS Trust , South Warwickshire NHS Trust and Cambridge Hospital University NHS Foundation Trust. Full ophthalmology data for all service providers is required however to allow comparison against all other Trusts, in relation to key metrics such as 18 week waiting time targets, 28 day emergency readmissions, did not attends and cancellations. Quarterly updates are required so that these Trusts can carry out regular comparisons of their performance against other Trusts. The risk of any re-identification will be mitigated by only including counts or estimated averages in outputs. The HSCIC HES Analysis Guide includes guidance on the suppression of small numbers, and these rules will be applied to all the simulation and forecasted outputs.	Pathway Communications will receive HES data from the HSCIC related to Ophthalmology episodes, filtered by treatment specialty 130 and all diagnosis codes returned within that treatment specialty. 1. Forecasting Demand for Ophthalmology Services Pathway Communications will look at the number of inpatient admissions (elective/non-elective), outpatient attendances, follow-ups, did not attends and cancellations. These will be counted for each month (i.e. 36 historical data points) where forecasting algorithms will be used to predict demand for services over the next 12 months. 2. Ophthalmology Pathway Simulation Pathway Communications will count the number of arrivals (and mode of arrival) for these Trusts' ophthalmology service, including individual patient’s footsteps in Inpatient and Outpatient. Additional information will be extracted, including average length of stay, average waiting time for an Outpatient appointment, average number of follow-ups and average cost of care (HRG codes). These will then become key input parameters for the simulation model (discrete event simulation using SIMUL8). Forecasted activity will also be embedded into the simulation model.	1. Forecasting Demand for Ophthalmology Services The forecasting tool will only output forecasted activity for services over the next 12 months. The forecasted activity will include inpatient admissions, outpatient attendances. The outputs from Forecasting will be restricted to static reports and small numbers not suppressed as it is a prediction and not the data itself. Pathway Communications will forecast monthly first attendances, follow-ups, DNAs and cancellations. These forecasts will not produce indicators showing performance of the Trust. Target date: the tool will be complete 3 months after HES is available 2. Ophthalmology Pathway Simulation The outputs from HES associated with “ophthalmology pathway simulation are the estimated summary statistics used as input parameters in to the simulation model (i.e. the average length of stay, average cost of the pathway using HRG codes, number of admissions/attendances/DNAs/cancellations and follow-ups). The simulation will output metrics associated with: • Staff utilization (in percentages) • Bed/theatre/clinic slot utilization (in percentages) • Diagnostic/Treatment procedures (in numbers) • Monthly waiting time (in weeks) • Monthly revenue and costs (in pounds) • Comparison of these Trusts against all other Trusts in relation to waiting time and readmissions (in weeks and numbers) These simulations will enable rapid exploration of future service capacity and demand across the Trust. The outputs from the simulation will be Powerpoint graphs and charts in pdf format. They will not contain any record level HES data, only aggregated data (with small numbers suppressed in line with the HES Analysis Guide). These simulations will produce indicators showing performance of these Trusts against other Trusts. Target date: the simulation will be complete 3 months after HES is available	Without building the simulations and forecasting tool and populating them with the HES data it is impossible to predict what specific measurable benefits these Trusts will experience. However the key purpose of the development is to enable numerous different service improvement options to be explored and understood. The forecasting and ophthalmology patient pathway simulations will both provide useful tools for forecasting future activity for these Trusts. Often such forecasts take the Trust months to prepare and are not statistically validated, resulting in inaccurate predictions. The forecasting and simulations will replace this with a few minutes’ work. As far as Pathway Communications are aware, the combination of forecasting with simulation is not available to the NHS. The forecasting will: • Enable these Trusts to better understand their demand with accuracies in the region of 90%, hence the opportunity to plan well ahead into the future with much greater reliability than before. • update these Trusts demand predictions on a quarterly basis, which will alert the management if there is an expected surge of inpatient admissions/attendances. • Uncover demand and capacity issues instantly to enable quicker reaction to changes in acute activity so that necessary measures can be in place to ensure effective delivery of care at all times to ensure continuity of care to patients. The Ophthalmology Pathway Simulation for these Trusts will: • enable the full impact of changes to these Trusts ophthalmology service to be explored in the safety of a ‘virtual’ environment. At present due to the complexity in ophthalmology services, changes are introduced without a full understanding of the impact they will have on other areas of the service. • the outputs from the simulation (the results of the impact on resources, numbers of patient treated etc.) will be able to be used to support strong business cases for change to board-level executives and holders of budgets, thus dramatically shortening the time from identifying the service improvements to be made, to implementing the improvements. Currently this can often take around a year. Using the simulation, these Trusts will be able to shorten this to a matter of a few weeks. • the simulation will be fully statistically validated so that the results can be relied on to be accurate. At present service improvements are often based on subjective opinions of what improvements should be made. Pathway Communications will require 3 months after HES data is available, to complete building the simulation and forecasts for these Trusts and expect the Trusts to start to benefit from using these tools 6-8 weeks after this.
PRIVATE HEALTHCARE INFORMATION NETWORK (PHIN)	PRIVATE HEALTHCARE INFORMATION NETWORK (PHIN)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	PHIN has been appointed by the Competition and Markets Authority (CMA) to the role of ‘Information Organisation’ charged with implementing the remedies set out in the Private Healthcare Market Investigation Order 2014. The Order requires every operator of a private healthcare facility to supply PHIN with “information as regards every patient episode of all private patients treated at that facility, and data which is sufficiently detailed and complete to enable the information organisation to publish [a specified list] of performance measures by procedure at both hospital and consultant level.” [Private Healthcare Market Investigation Order 2014, Article 21.1] The Order required PHIN to “prepare and submit to the CMA for approval a five-year plan, which has been developed in conjunction with, and approved by, its members, setting out how it proposes to collect the information specified in this Order and the basis on which it may licence access to this information” [Article 24.1]. In response, PHIN produced its Strategic Plan 2015-2020 which outlines its proposals for implementing the remedies. CMA approved the plan. PHIN’s approved plan requires routine extracts of HES data from the HSCIC. This will include the necessary data on NHS-funded care delivered in independent hospitals and privately-funded episodes delivered in NHS hospitals. Receiving this data from HES, as opposed to directly from the private healthcare facilities, is advantageous both in minimising the duplication of data and data transmission routes – meaning that care providers will only have to transmit their information once (reducing both effort and the possibility of data transmission errors) – and crucially in ensuring consistency in data quality. Receiving HES data mitigates the risk of inconsistencies in the data resulting from PHIN and the HSCIC processing the raw data in different ways if both were to receive it directly from care providers. HES also contains important data that PHIN cannot replace by direct submission; this is principally the NHS-funded NHS-provided data comprising 85% of elective care activity that PHIN needs to provide the benchmarks against which to compare the private sector. That data is needed at hospital, consultant and procedure level. To report the whole of a consultants’ practice, it is necessary to consider the work that they do within the NHS as well as privately. The CMA’s Final Report states its expectation that data submitted by the private hospital operators to the information organisation (PHIN) should be “be fully comparable with that collected by the NHS to allow the information organization to report performance measures for the whole of consultants’ practices, both NHS and private, since this is the relevant basis on which to judge performance” [Article 11.486]. To achieve this PHIN must include in that comparison the 85% of elective episodes that are both NHS funded and provided. Hence data is needed for the NHS episodes. Although PHIN’s interest in consultants’ practice within the NHS is limited to those consultants that also have a private practice, PHIN need to receive all NHS episodes (non-emergency APC), because the HSCIC cannot determine from the data it holds which episodes belong to consultants who practice privately (where that private practice is conducted outside the NHS). This group of consultants is also a constantly changing population and as such it is not practical for PHIN to seek to amend the list of consultants for which it requires data, as gaps in a consultant’s data may be as a result of leave or switching from one private hospital to another. However, PHIN will not produce or publish indicators on consultants that do not have a private practice and such consultants will not be able to view their data via PHIN’s portal. Continuous inactivity will trigger the removal of a consultant’s indicator from the site. PHIN will establish the following rules to manage the circumstances where a consultant has either been inactive for a prolonged period of time or is no longer on the live list of GMC registered consultants. 1. Each month PHIN’s systems will automatically check the latest available GMC registration status of all consultants contained within PHIN’s database. Only data for “live” consultants (Registration Status is “Registered with a licence”) will be processed and published on the public web site. 2. As soon as there is 12 continuous months of no Admitted Patient Care data within the database for a particular consultant, they will automatically be removed from the public web site. This will apply to all their indicators as well as their public profile. 3. In such circumstances the consultant will be automatically notified by email of this event and PHIN's intention to remove them from the site. Unless PHIN receives a communication challenging this action, they will be removed. 4. If activity subsequently appears for the same consultant then they will automatically re-appear within the web site and an email will notify them of this fact. PHIN also requires linked HES/PROMS data to deliver “procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate” (CMA Order Article 21.1(j)). The CMA’s Final Report states: 11.571 In order to facilitate the analysis and publication of meaningful performance statistics, we would expect the data provided by the private hospital operators to: c) contain diagnostic and procedure coding1096 for each episode in order to allow for risk-adjustment where appropriate—diagnostic coding should include full details of patient co-morbidities; In addition to the public access to the PHIN website that everybody will have, PHIN will grant licensed access to its information to consultants and to providers of private care. Secure, authenticated access will be granted to information specific and appropriate to the particular consultant: this will include detailed (but pseudonymised) views of the care for which they are directly responsible, alongside relevant totals, averages and benchmarks. Commercial confidentiality will be respected alongside patient confidentiality, and no party will have inappropriate access to details related to their peers and competitors. As such, no party will have access to the “database”. Users will be required to accept licence terms covering information governance, intellectual property and so on when they access the Portal. There will be no charge for this licensed access. This service is not explicitly mandated by the CMA Order but is described on pages 14 and 17 of the PHIN Strategic Plan 2015-2020. As stated in the Chairman’s foreword (p.3), one of PHIN’s aims is to “help private providers continuously improve their care and clinical outcomes”. PHIN will do this by enabling them “to see and understand performance measures in context including with peer group benchmarks” (p14). This is the type of service routinely provided within the NHS by the HSCIC (NHS Comparators), Dr Foster and many other means. However, there has never been any central collation of data in private healthcare and hence no information on comparative performance from which to learn. The lack of that information particularly disadvantages the standalone (usually charitable) hospitals, as the larger national providers can at least compare between their own hospitals. It has also meant that the CQC has a very limited view of private activity and quality, and the CQC has asked PHIN to help address that gap. Confidence in the data underpinning any analysis and performance measurement is essential. For that reason, it must be open to scrutiny and checking, such that providers and consultants can validate that the data is accurate and fix it where it is not. It is for that purpose that PHIN make the record-level data available for checking, only to the providers and consultants with whom the data originated as those responsible for the episode. They can then assure themselves that all of their data is present and accurately recorded.	Periods of Data Requested At a minimum, PHIN will need data from 2013/14 onwards. This will enable it to begin analysis now on a complete 24 months of “official” HES data and ensure alignment and testing of case mix adjustment methods, their validation by relevant expert bodies and socialising the outputs with all relevant stakeholders - in particular the organisations and individuals covered by the order (approximately 500 private hospital operators and 12,500 consultants). The date by which PHIN must publish information for patients is 30 April 2017 and will be based on data for the calendar year 2016. Frequency of Data Requested HES data will be required on a monthly frequency. Although the CMA states only a quarterly data extract from the private hospital operators, PHIN’s view, noting that the NHS data processing to produce HES takes at least three months, is that in order for the data to describe the most current view of hospital and consultant activity it requires monthly updates. This frequency also coincides with the extract schedule for data received from the independent hospitals. The indicators on the website will be updated on a monthly basis so it would be optimum to synchronise the frequencies of data collection and reduce delays to data availability as much as possible. Furthermore, and to address the “provisional” nature of this data, PHIN is also requesting Annual Refreshes each year. Periodic indicators will cover a rolling 12-month period and will be recalculated to reflect the final data. Data Storage and Processing PHIN will not perform any record level linkage between the requested data and any other patient data it currently holds or may hold or have access to in the future. The data will only be used to fulfil the CMA requirement to provide information for the general public, and data will not be provided to private healthcare providers that could then be used for sales or marketing purposes. PHIN stores and processes data in accordance with its Information Governance Policy, which is aligned to the NHS IG Toolkit and HES licence terms in the form of a System Level Security Policy. All record level data requested under this application will be stored on PHIN’s own servers hosted in Claranet’s ISO27001-accredited data centre in Bristol. Record-level data is held only electronically within this data centre. Access to record-level data is strictly limited to nominated PHIN-authorised persons required to process or check it. All database administration and Extract, Transform and Load (ETL) processing is under the control of named PHIN-authorised staff who can only access this data via a dedicated internet connection into PHIN’s offices. All computers accessing the data centre are located in PHIN’s offices, are password protected, encrypted and owned and administered by PHIN. PHIN uses SQL Server 2012 to store data and Tableau to analyse and report on it. In order to ensure consistency in methodology with other publicly available indicators (e.g. NHS Choices), PHIN agree to work with the HSCIC’s assurance processes to ensure their methodology is suitable peer-reviewed. Indicators based on aggregate data will be published on PHIN’s public facing website (www.phin.org.uk) through a series of reports. PHIN has a staging server that is only accessible internally where record level data is loaded and aggregate data processed. It has a SQL server port open and an ETL process that moves the output aggregate tables onto an output database server, which resides on internal Virtual Local Area Networks within PHIN’s network. The third layer is the web server that runs the website. This is accessed (controlled by firewall) through specific ports over the internet (different depending on whether the site requires an SSL certificate and is HTTP or HTTPS). The website can then communicate (through another firewall) with the output database server through the appropriate SQL port. Combining HES and PHES Data For the purposes of whole-hospital reporting, where an independent hospital is also providing NHS-funded care, HES data and the data describing the treatment of private patients in an independent hospital (PHES) data will be combined under the hospital’s site code. No patient or record-level data linkage will occur between these two datasets. For the purposes of consultant whole-practice reporting where a consultant provides care in both independent and NHS hospitals (where they are identified within the data as being the responsible consultant), HES and PHES data will be combined under the consultant’s GMC code. No patient or record-level data linkage will occur between these two datasets. For the avoidance of doubt, the PHES dataset that will used for the two purposes listed above will be pseudonymised in terms of patient identifiable data. Performance indicators produced as a result of combining the English PHES and HES data will only be compared to similar results for hospitals in Wales, Scotland and Northern Ireland. HES data will not be combined with its equivalent data from any of these Nations. Data Destruction PHIN will permanently destroy all record level HES data submitted as part of the monthly data flows as soon as it receives the associated Annual Refresh data. PHIN will hold a maximum of five years of finalised, annual data at any time, destroying older data on a rolling basis. Risk Adjustment and Standardisation Subject to the availability and quality of certain relevant data (e.g. diagnoses, patient age etc.), indicators will be adjusted with respect to these variables and calculated using appropriate statistical methodologies in order to support comparative analysis and presentation. Wherever possible such adjustments will be guided by NHS best practice in order to enable comparisons within and across healthcare sectors. The CMA Order requires PHIN to subject these and all its methodologies to external, independent scrutiny (see CMA Order Article 24.5). Calculation of Performance Indicators The HES data will be used to generate the indicators prescribed in the CMA Order, based on pseudonymised aggregated or combined PHES and HES data, conforming to rules on small number suppression and available at hospital, consultant and procedure level. The information will be published as indicators on a publically accessible website (www.phin.org.uk): • Volumes of procedures • Length of stay Similarly, procedure specific NHS-funded PROMS data will be used to generate the indicators prescribed in the CMA Order, based on pseudonymised aggregated or combined private and NHS-funded PROMS data, conforming to rules on small number suppression and available at hospital, consultant and procedure level. The information will be published as indicators on a publically accessible website (www.phin.org.uk): • Procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate All outputs will be aggregated and will consist of respectively, relative values, median values and “scores”. Small number suppression will be applied where required in line with the HES Analysis Guide.	PHIN will calculate and publish on its public facing website (www.phin.org.uk) the following indicators derived from a combination of HES and PHES data: • volumes of procedures undertaken (by hospital and by consultant); and • average lengths of stay for each procedure (by hospital and by consultant). • Procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate Indicators will be presented as iconic, graphical and numerical visualisations, similar to NHS Choices and other public health websites, with the specific calculated values for the selected hospital or consultant presented within a statistically robust and comparative context which will include a sector average. Each indicator will be accompanied by interpretive and methodological information. Each indicator will also include explanatory information and descriptive information for each hospital and consultant. Information for the Public Members of the public will access the performance indicators at www.phin.org.uk. Indicators containing the requested data will be presented as iconic, graphical and numerical visualisations, with calculated values for each hospital and consultant presented within a statistically robust and comparative context which will include one or both of an independent sector and a NHS sector average and conforming to rules on small number suppression. Where appropriate, indicators will be risk adjusted using methodologies approved by relevant clinical and/or academic bodies. The CMA Order requires PHIN to subject these and all its methodologies to external, independent scrutiny (see CMA Order Article 24.5). Each indicator will be accompanied by interpretive and methodological information and each hospital and consultant will be accompanied by descriptive information drawn from other data sources but independent of and not linked to the data requested under this application. Data Quality and Data Validation by Private Healthcare Facilities For the sole and specific purpose of data quality and data validation, each of the three types of performance indicator prescribed in the CMA Order together with the underpinning pseudonymised record level data will be accessible to each private healthcare facility and to authorised individuals from PHIN. Essentially this will entail the hospital confirming that the numerator and denominator values are correct for each of their procedures that is going to appear on the public web site. Time series analyses of the data will also help reveal unexpected patterns that may point to missing data. The portal within which this process will take place will include functionality for queries against the data to be automatically directed to the relevant (authorised) individual from the hospital site or group in question. Such queries will provide specific feedback on the highlighted issue and workflow will track their subsequent resolution and outcome. If necessary, data will be corrected at source and refreshes passed through to PHIN as part of the routine data submission process. This data will be made available through an online reporting tool via a secure portal requiring a validated username and password. User access will be granted in line with agreed data sharing protocols, where appropriate this will be approved by the local Caldicott Guardian. User’s login credentials will restrict the data to which each user has access, which means that users from specific hospitals will only be able to see record level data originating from their hospital. All record level data will be pseudonymised and contain no patient identifiable data and all data will at all times remain solely on PHIN’s servers – it will not be possible for users to move this data to another location. This data has no commercial value in that it relates solely to the care which the hospital itself provided. Data Quality and Data Validation by Consultants with NHS and Private Practice For the sole and specific purpose of data quality and data validation, each of the three types of performance indicator prescribed in the CMA Order together with the underpinning pseudonymised record level data will be accessible to each consultant and to authorised individuals from PHIN. The process whereby this data validation takes place is the same as that described above for hospitals. This data will be made available through an online reporting tool via a secure portal requiring a validated username and password. User’s credentials will restrict the data to which each has access, which means that a specific consultant will only be able to access his or her indicators and associated record level data. Furthermore, this validation process, whereby PHIN will require consultants to actively opt-in to having their activity published as performance indicators (by means of an electronic sign-off), may have the beneficial effect to the NHS of having consultants checking their HES data for errors for the first time. All record level data will be pseudonymised and contain no patient identifiable data and all data will at all times remain solely on PHIN’s servers – it will not be possible for users to move this data to another location. This data has no commercial value in that it relates solely to activity for which they were identified within the data as the responsible clinician. Timeline for Publication The CMA Report and associated Order requires that its indicators will be published from April 2017 onwards and that they must be based on at least 12 months of data. Thus PHIN has between now and January 2016 to develop and evaluate its indicator methodologies, from which point onwards all data flows must be identified, established and operational.	PHIN’s over-arching mission is two-fold: to enable patients to be able to make better informed choices about their healthcare providers and, through the provision of comparative information, to help private providers continuously improve their care and clinical outcomes. Whilst a small proportion (around 5%) of the 10 million or so patients encountering the UK independent hospital sector annually come from overseas, usually in a handful of central London hospitals, the overwhelming majority of patients are also NHS patients for most of their care (GP, maternity, A&E, end of life, emergency and most elective etc.), simply opting to take some elective care privately. Crucially, neither HSCIC nor NHS Choices has access to private episode data from independent hospitals (Private HES or “PHES” data), nor a mandate nor funding that would enable them to collect that data to form a full view of the private hospitals from which NHS funded care may also be being commissioned and delivered. Consequently, for example, the CQC has found that the data required to inform proper regulation is not routinely available for independent hospitals as it is for NHS providers. The CMA Order enables PHIN to licence this PHES data to interested external third parties to support information gaps such as these. Consequently, the Health and Social Care system has no means of properly understanding private healthcare including, for example, determining the extent to which patient deaths or complications following treatment in the private sector places a burden on the NHS when they result in emergency admissions into NHS hospitals. Similarly it is blind to the extent to which private patients require an emergency transfer of care to the NHS. PHIN aims to fill those gaps and address those deficiencies, by the methods described above, for the benefit of patients. Furthermore, as Patient Choice frequently includes NHS funded treatment in a private hospital, the PHIN website will be the only source of information for these patients which describes the totality of care provided by these hospitals, being the combination of their private and NHS funded activity. This is a much more comprehensive indication of their performance than the partial information available from the NHS Choices site which is only based on the NHS funded element of their workload. PHIN’s use of the data requested in this application will therefore facilitate new understanding and inform quality improvements within the private healthcare sector, along with facilitating improved regulation, commissioning and policy making, leading to improvements in the quality and management of care that will benefit UK citizens and tax payers generally.
PRIVATE HEALTHCARE INFORMATION NETWORK (PHIN)	PRIVATE HEALTHCARE INFORMATION NETWORK (PHIN)	Patient Reported Outcome Measures (Linkable to HES)	Anonymised - ICO code compliant	Sensitive	Health and Social Care Act 2012	Ongoing	N	PHIN has been appointed by the Competition and Markets Authority (CMA) to the role of ‘Information Organisation’ charged with implementing the remedies set out in the Private Healthcare Market Investigation Order 2014. The Order requires every operator of a private healthcare facility to supply PHIN with “information as regards every patient episode of all private patients treated at that facility, and data which is sufficiently detailed and complete to enable the information organisation to publish [a specified list] of performance measures by procedure at both hospital and consultant level.” [Private Healthcare Market Investigation Order 2014, Article 21.1] The Order required PHIN to “prepare and submit to the CMA for approval a five-year plan, which has been developed in conjunction with, and approved by, its members, setting out how it proposes to collect the information specified in this Order and the basis on which it may licence access to this information” [Article 24.1]. In response, PHIN produced its Strategic Plan 2015-2020 which outlines its proposals for implementing the remedies. CMA approved the plan. PHIN’s approved plan requires routine extracts of HES data from the HSCIC. This will include the necessary data on NHS-funded care delivered in independent hospitals and privately-funded episodes delivered in NHS hospitals. Receiving this data from HES, as opposed to directly from the private healthcare facilities, is advantageous both in minimising the duplication of data and data transmission routes – meaning that care providers will only have to transmit their information once (reducing both effort and the possibility of data transmission errors) – and crucially in ensuring consistency in data quality. Receiving HES data mitigates the risk of inconsistencies in the data resulting from PHIN and the HSCIC processing the raw data in different ways if both were to receive it directly from care providers. HES also contains important data that PHIN cannot replace by direct submission; this is principally the NHS-funded NHS-provided data comprising 85% of elective care activity that PHIN needs to provide the benchmarks against which to compare the private sector. That data is needed at hospital, consultant and procedure level. To report the whole of a consultants’ practice, it is necessary to consider the work that they do within the NHS as well as privately. The CMA’s Final Report states its expectation that data submitted by the private hospital operators to the information organisation (PHIN) should be “be fully comparable with that collected by the NHS to allow the information organization to report performance measures for the whole of consultants’ practices, both NHS and private, since this is the relevant basis on which to judge performance” [Article 11.486]. To achieve this PHIN must include in that comparison the 85% of elective episodes that are both NHS funded and provided. Hence data is needed for the NHS episodes. Although PHIN’s interest in consultants’ practice within the NHS is limited to those consultants that also have a private practice, PHIN need to receive all NHS episodes (non-emergency APC), because the HSCIC cannot determine from the data it holds which episodes belong to consultants who practice privately (where that private practice is conducted outside the NHS). This group of consultants is also a constantly changing population and as such it is not practical for PHIN to seek to amend the list of consultants for which it requires data, as gaps in a consultant’s data may be as a result of leave or switching from one private hospital to another. However, PHIN will not produce or publish indicators on consultants that do not have a private practice and such consultants will not be able to view their data via PHIN’s portal. Continuous inactivity will trigger the removal of a consultant’s indicator from the site. PHIN will establish the following rules to manage the circumstances where a consultant has either been inactive for a prolonged period of time or is no longer on the live list of GMC registered consultants. 1. Each month PHIN’s systems will automatically check the latest available GMC registration status of all consultants contained within PHIN’s database. Only data for “live” consultants (Registration Status is “Registered with a licence”) will be processed and published on the public web site. 2. As soon as there is 12 continuous months of no Admitted Patient Care data within the database for a particular consultant, they will automatically be removed from the public web site. This will apply to all their indicators as well as their public profile. 3. In such circumstances the consultant will be automatically notified by email of this event and PHIN's intention to remove them from the site. Unless PHIN receives a communication challenging this action, they will be removed. 4. If activity subsequently appears for the same consultant then they will automatically re-appear within the web site and an email will notify them of this fact. PHIN also requires linked HES/PROMS data to deliver “procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate” (CMA Order Article 21.1(j)). The CMA’s Final Report states: 11.571 In order to facilitate the analysis and publication of meaningful performance statistics, we would expect the data provided by the private hospital operators to: c) contain diagnostic and procedure coding1096 for each episode in order to allow for risk-adjustment where appropriate—diagnostic coding should include full details of patient co-morbidities; In addition to the public access to the PHIN website that everybody will have, PHIN will grant licensed access to its information to consultants and to providers of private care. Secure, authenticated access will be granted to information specific and appropriate to the particular consultant: this will include detailed (but pseudonymised) views of the care for which they are directly responsible, alongside relevant totals, averages and benchmarks. Commercial confidentiality will be respected alongside patient confidentiality, and no party will have inappropriate access to details related to their peers and competitors. As such, no party will have access to the “database”. Users will be required to accept licence terms covering information governance, intellectual property and so on when they access the Portal. There will be no charge for this licensed access. This service is not explicitly mandated by the CMA Order but is described on pages 14 and 17 of the PHIN Strategic Plan 2015-2020. As stated in the Chairman’s foreword (p.3), one of PHIN’s aims is to “help private providers continuously improve their care and clinical outcomes”. PHIN will do this by enabling them “to see and understand performance measures in context including with peer group benchmarks” (p14). This is the type of service routinely provided within the NHS by the HSCIC (NHS Comparators), Dr Foster and many other means. However, there has never been any central collation of data in private healthcare and hence no information on comparative performance from which to learn. The lack of that information particularly disadvantages the standalone (usually charitable) hospitals, as the larger national providers can at least compare between their own hospitals. It has also meant that the CQC has a very limited view of private activity and quality, and the CQC has asked PHIN to help address that gap. Confidence in the data underpinning any analysis and performance measurement is essential. For that reason, it must be open to scrutiny and checking, such that providers and consultants can validate that the data is accurate and fix it where it is not. It is for that purpose that PHIN make the record-level data available for checking, only to the providers and consultants with whom the data originated as those responsible for the episode. They can then assure themselves that all of their data is present and accurately recorded.	Periods of Data Requested At a minimum, PHIN will need data from 2013/14 onwards. This will enable it to begin analysis now on a complete 24 months of “official” HES data and ensure alignment and testing of case mix adjustment methods, their validation by relevant expert bodies and socialising the outputs with all relevant stakeholders - in particular the organisations and individuals covered by the order (approximately 500 private hospital operators and 12,500 consultants). The date by which PHIN must publish information for patients is 30 April 2017 and will be based on data for the calendar year 2016. Frequency of Data Requested HES data will be required on a monthly frequency. Although the CMA states only a quarterly data extract from the private hospital operators, PHIN’s view, noting that the NHS data processing to produce HES takes at least three months, is that in order for the data to describe the most current view of hospital and consultant activity it requires monthly updates. This frequency also coincides with the extract schedule for data received from the independent hospitals. The indicators on the website will be updated on a monthly basis so it would be optimum to synchronise the frequencies of data collection and reduce delays to data availability as much as possible. Furthermore, and to address the “provisional” nature of this data, PHIN is also requesting Annual Refreshes each year. Periodic indicators will cover a rolling 12-month period and will be recalculated to reflect the final data. Data Storage and Processing PHIN will not perform any record level linkage between the requested data and any other patient data it currently holds or may hold or have access to in the future. The data will only be used to fulfil the CMA requirement to provide information for the general public, and data will not be provided to private healthcare providers that could then be used for sales or marketing purposes. PHIN stores and processes data in accordance with its Information Governance Policy, which is aligned to the NHS IG Toolkit and HES licence terms in the form of a System Level Security Policy. All record level data requested under this application will be stored on PHIN’s own servers hosted in Claranet’s ISO27001-accredited data centre in Bristol. Record-level data is held only electronically within this data centre. Access to record-level data is strictly limited to nominated PHIN-authorised persons required to process or check it. All database administration and Extract, Transform and Load (ETL) processing is under the control of named PHIN-authorised staff who can only access this data via a dedicated internet connection into PHIN’s offices. All computers accessing the data centre are located in PHIN’s offices, are password protected, encrypted and owned and administered by PHIN. PHIN uses SQL Server 2012 to store data and Tableau to analyse and report on it. In order to ensure consistency in methodology with other publicly available indicators (e.g. NHS Choices), PHIN agree to work with the HSCIC’s assurance processes to ensure their methodology is suitable peer-reviewed. Indicators based on aggregate data will be published on PHIN’s public facing website (www.phin.org.uk) through a series of reports. PHIN has a staging server that is only accessible internally where record level data is loaded and aggregate data processed. It has a SQL server port open and an ETL process that moves the output aggregate tables onto an output database server, which resides on internal Virtual Local Area Networks within PHIN’s network. The third layer is the web server that runs the website. This is accessed (controlled by firewall) through specific ports over the internet (different depending on whether the site requires an SSL certificate and is HTTP or HTTPS). The website can then communicate (through another firewall) with the output database server through the appropriate SQL port. Combining HES and PHES Data For the purposes of whole-hospital reporting, where an independent hospital is also providing NHS-funded care, HES data and the data describing the treatment of private patients in an independent hospital (PHES) data will be combined under the hospital’s site code. No patient or record-level data linkage will occur between these two datasets. For the purposes of consultant whole-practice reporting where a consultant provides care in both independent and NHS hospitals (where they are identified within the data as being the responsible consultant), HES and PHES data will be combined under the consultant’s GMC code. No patient or record-level data linkage will occur between these two datasets. For the avoidance of doubt, the PHES dataset that will used for the two purposes listed above will be pseudonymised in terms of patient identifiable data. Performance indicators produced as a result of combining the English PHES and HES data will only be compared to similar results for hospitals in Wales, Scotland and Northern Ireland. HES data will not be combined with its equivalent data from any of these Nations. Data Destruction PHIN will permanently destroy all record level HES data submitted as part of the monthly data flows as soon as it receives the associated Annual Refresh data. PHIN will hold a maximum of five years of finalised, annual data at any time, destroying older data on a rolling basis. Risk Adjustment and Standardisation Subject to the availability and quality of certain relevant data (e.g. diagnoses, patient age etc.), indicators will be adjusted with respect to these variables and calculated using appropriate statistical methodologies in order to support comparative analysis and presentation. Wherever possible such adjustments will be guided by NHS best practice in order to enable comparisons within and across healthcare sectors. The CMA Order requires PHIN to subject these and all its methodologies to external, independent scrutiny (see CMA Order Article 24.5). Calculation of Performance Indicators The HES data will be used to generate the indicators prescribed in the CMA Order, based on pseudonymised aggregated or combined PHES and HES data, conforming to rules on small number suppression and available at hospital, consultant and procedure level. The information will be published as indicators on a publically accessible website (www.phin.org.uk): • Volumes of procedures • Length of stay Similarly, procedure specific NHS-funded PROMS data will be used to generate the indicators prescribed in the CMA Order, based on pseudonymised aggregated or combined private and NHS-funded PROMS data, conforming to rules on small number suppression and available at hospital, consultant and procedure level. The information will be published as indicators on a publically accessible website (www.phin.org.uk): • Procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate All outputs will be aggregated and will consist of respectively, relative values, median values and “scores”. Small number suppression will be applied where required in line with the HES Analysis Guide.	PHIN will calculate and publish on its public facing website (www.phin.org.uk) the following indicators derived from a combination of HES and PHES data: • volumes of procedures undertaken (by hospital and by consultant); and • average lengths of stay for each procedure (by hospital and by consultant). • Procedure-specific measures of improvement in health outcomes, as agreed by the information organisation and its members to be appropriate Indicators will be presented as iconic, graphical and numerical visualisations, similar to NHS Choices and other public health websites, with the specific calculated values for the selected hospital or consultant presented within a statistically robust and comparative context which will include a sector average. Each indicator will be accompanied by interpretive and methodological information. Each indicator will also include explanatory information and descriptive information for each hospital and consultant. Information for the Public Members of the public will access the performance indicators at www.phin.org.uk. Indicators containing the requested data will be presented as iconic, graphical and numerical visualisations, with calculated values for each hospital and consultant presented within a statistically robust and comparative context which will include one or both of an independent sector and a NHS sector average and conforming to rules on small number suppression. Where appropriate, indicators will be risk adjusted using methodologies approved by relevant clinical and/or academic bodies. The CMA Order requires PHIN to subject these and all its methodologies to external, independent scrutiny (see CMA Order Article 24.5). Each indicator will be accompanied by interpretive and methodological information and each hospital and consultant will be accompanied by descriptive information drawn from other data sources but independent of and not linked to the data requested under this application. Data Quality and Data Validation by Private Healthcare Facilities For the sole and specific purpose of data quality and data validation, each of the three types of performance indicator prescribed in the CMA Order together with the underpinning pseudonymised record level data will be accessible to each private healthcare facility and to authorised individuals from PHIN. Essentially this will entail the hospital confirming that the numerator and denominator values are correct for each of their procedures that is going to appear on the public web site. Time series analyses of the data will also help reveal unexpected patterns that may point to missing data. The portal within which this process will take place will include functionality for queries against the data to be automatically directed to the relevant (authorised) individual from the hospital site or group in question. Such queries will provide specific feedback on the highlighted issue and workflow will track their subsequent resolution and outcome. If necessary, data will be corrected at source and refreshes passed through to PHIN as part of the routine data submission process. This data will be made available through an online reporting tool via a secure portal requiring a validated username and password. User access will be granted in line with agreed data sharing protocols, where appropriate this will be approved by the local Caldicott Guardian. User’s login credentials will restrict the data to which each user has access, which means that users from specific hospitals will only be able to see record level data originating from their hospital. All record level data will be pseudonymised and contain no patient identifiable data and all data will at all times remain solely on PHIN’s servers – it will not be possible for users to move this data to another location. This data has no commercial value in that it relates solely to the care which the hospital itself provided. Data Quality and Data Validation by Consultants with NHS and Private Practice For the sole and specific purpose of data quality and data validation, each of the three types of performance indicator prescribed in the CMA Order together with the underpinning pseudonymised record level data will be accessible to each consultant and to authorised individuals from PHIN. The process whereby this data validation takes place is the same as that described above for hospitals. This data will be made available through an online reporting tool via a secure portal requiring a validated username and password. User’s credentials will restrict the data to which each has access, which means that a specific consultant will only be able to access his or her indicators and associated record level data. Furthermore, this validation process, whereby PHIN will require consultants to actively opt-in to having their activity published as performance indicators (by means of an electronic sign-off), may have the beneficial effect to the NHS of having consultants checking their HES data for errors for the first time. All record level data will be pseudonymised and contain no patient identifiable data and all data will at all times remain solely on PHIN’s servers – it will not be possible for users to move this data to another location. This data has no commercial value in that it relates solely to activity for which they were identified within the data as the responsible clinician. Timeline for Publication The CMA Report and associated Order requires that its indicators will be published from April 2017 onwards and that they must be based on at least 12 months of data. Thus PHIN has between now and January 2016 to develop and evaluate its indicator methodologies, from which point onwards all data flows must be identified, established and operational.	PHIN’s over-arching mission is two-fold: to enable patients to be able to make better informed choices about their healthcare providers and, through the provision of comparative information, to help private providers continuously improve their care and clinical outcomes. Whilst a small proportion (around 5%) of the 10 million or so patients encountering the UK independent hospital sector annually come from overseas, usually in a handful of central London hospitals, the overwhelming majority of patients are also NHS patients for most of their care (GP, maternity, A&E, end of life, emergency and most elective etc.), simply opting to take some elective care privately. Crucially, neither HSCIC nor NHS Choices has access to private episode data from independent hospitals (Private HES or “PHES” data), nor a mandate nor funding that would enable them to collect that data to form a full view of the private hospitals from which NHS funded care may also be being commissioned and delivered. Consequently, for example, the CQC has found that the data required to inform proper regulation is not routinely available for independent hospitals as it is for NHS providers. The CMA Order enables PHIN to licence this PHES data to interested external third parties to support information gaps such as these. Consequently, the Health and Social Care system has no means of properly understanding private healthcare including, for example, determining the extent to which patient deaths or complications following treatment in the private sector places a burden on the NHS when they result in emergency admissions into NHS hospitals. Similarly it is blind to the extent to which private patients require an emergency transfer of care to the NHS. PHIN aims to fill those gaps and address those deficiencies, by the methods described above, for the benefit of patients. Furthermore, as Patient Choice frequently includes NHS funded treatment in a private hospital, the PHIN website will be the only source of information for these patients which describes the totality of care provided by these hospitals, being the combination of their private and NHS funded activity. This is a much more comprehensive indication of their performance than the partial information available from the NHS Choices site which is only based on the NHS funded element of their workload. PHIN’s use of the data requested in this application will therefore facilitate new understanding and inform quality improvements within the private healthcare sector, along with facilitating improved regulation, commissioning and policy making, leading to improvements in the quality and management of care that will benefit UK citizens and tax payers generally.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The data supplied by the NHSIC to Public Health England will be used only for the approved Medical Research Project MR1045.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The data supplied by the NHSIC to Public Health England will be used only for the approved Medical Research Project MR1045.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	Enhanced surveillance of individuals identified as at increased risk of vCJD/CJD in the UK due to iatrogenic exposures or other indicators of increased risk. This is a long term follow up study of people who have been placed at additional risk of contracting CJD. The aim of this work is to ascertain any CJD-related deaths amongst individuals identified by actions recommended by the CJD Incidents Panel as at increased risk of CJD. This information is critical to: a) improve assessment of the risks experienced by these individuals, and others in similar circumstances, b) inform and evaluate public health measures relating to these individuals; c) investigate factors associated with transmission, disease and survival in these individuals.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	MRIS - Cohort Event Notification Report	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	Enhanced surveillance of individuals identified as at increased risk of vCJD/CJD in the UK due to iatrogenic exposures or other indicators of increased risk. This is a long term follow up study of people who have been placed at additional risk of contracting CJD. The aim of this work is to ascertain any CJD-related deaths amongst individuals identified by actions recommended by the CJD Incidents Panel as at increased risk of CJD. This information is critical to: a) improve assessment of the risks experienced by these individuals, and others in similar circumstances, b) inform and evaluate public health measures relating to these individuals; c) investigate factors associated with transmission, disease and survival in these individuals.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Cancer Registry linked to Diagnostic Imaging Dataset	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	To extend the information available for a cancer pathway, by linking data to Cancer Registry information
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Hospital Episode Statistics Accident and Emergency	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Hospital Episode Statistics Critical Care	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Hospital Episode Statistics Outpatients	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Patient level data relating to Diabetic Retinopathy Eye Screening FOR DIRECT CARE	Identifiable	Sensitive	Health and Social Care Act 2012	Ongoing	N	Objective for processing: The diabetic retinopathy screening program will use:• the demographic patient information transferred to invite people recently-diagnosed with diabetes for annual screening;• the relevant clinical information transferred about patients who attend clinic appointments so that appropriate patient care is provided to those patients;Processing activities:For each monthly cohort, all patients with diabetes codes (Diabetes Exclusions CST, Diabetes Resolved CST and Diabetes CST) will be identified by the HSCIC and only those who have 'Diabetes CST' as their latest code will then have demographic and clinical data extracted for them. The full list of all required demographic data will be extracted by the HSCIC alongside the latest diabetes code including the date and time of this record.The Data Provider Output (DPO) is the data that general practice system suppliers extract from general practice clinical systems and submit to the HSCIC. The DPO is based on the data extraction specification (referred to as the ‘Extraction Requirement’) that the HSCIC circulates to general practice system suppliers.There will be a small amount of data transformation performed on the DPO. The HSCIC will use the General Practice Extraction Tool - Query (GPET-Q) system to collate the general practices’ DPO data into a file and then send the resulting file to the HSCIC Data Management Environment (DME). This is the 'Full Cohort' extract.Two data attributes (NHS Number and Registered Practice Id) will be copied from the file into a further file which will be stored in the HSCIC DME. This is the 'Missing Patients' extract. It identifies the patients with diabetes extracted in the previous month’s extraction run, and the practices at which those patients were registered. Each month the HSCIC will replace the file with a new file of patient identifiers. Without that information it would be possible to send out confidential invitation letters inappropriately (e.g. to a person who has died, to the wrong address), potentially “lose” people in need of diabetic retinopathy services, and/or spend time and money investigating the "missing patients".The 'Missing Patients' and 'Full Cohort' extracts are the two data extracts that form the output files. The HSCIC processes and places the output files in a secure repository for Quicksilva (data processor for the 72 NHS diabetic retinopathy screening programmes) to download. The HSCIC retains the list of NHS numbers and GP practices until the next extract for the purpose of identifying the patients missing between months. This list will be destroyed once approved data about the missing patients has been extracted and sent to the 72 NHS diabetic retinopathy screening programmes. The data received will be stored within the central database of patient records maintained by the NHS diabetic retinopathy screening programme and used for the purposes described above.The screening database of patient records is maintained in data centres in Harrogate & London which are provided by Redcentric PLC (Redcentric PLC is certified in ISO27001, ISO9001 and NHS IG Toolkit 12 (commercial third party)). Quicksilva manage and operate the screening database and process the data to the requirements of the Public Health England. After processing activities, the data is sent using a N3 host to the 72 NHS diabetic retinopathy screening programmes dedicated systems located within secure care provider network environments (e.g. NHS trusts) and also sent to data reconciliation tools which updates patient demographic data with local clinical system data.In order that the right people get access to the right data, the screening database includes:• a mapping of the patient’s general practice to a local screening program;• role based access controls that limit what patient data can be accessed depending on a specific role;• and audit trails to detect any unauthorised and/or excessive access.In addition to the data provided to PHE, two data attributes (NHS Number and Practice) will be stored in the HSCIC DME to be used in future extractions Specific outputs expected, including target date:The data will be used on an ongoing basis within patient records maintained by the NHS diabetic retinopathy screening programme to invite patients for screening, and to enable clinicians to provide appropriate clinical care. This database will be updated monthly.Expected measurable benefits to health and/or social care including target date:See “03_GP2DRS - Benefits Plan - August 2013 (NIC-154590-YG6QH)”
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Standard Monthly Extract : SUS PbR A&E	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Standard Monthly Extract : SUS PbR APC Episodes	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Standard Monthly Extract : SUS PbR APC Spells	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Standard Monthly Extract : SUS PbR OP	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Diagnostic Imaging Dataset	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Mental Health Services Data Set	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	National Child Measurement Programme (NCMP)	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
PUBLIC HEALTH ENGLAND (PHE)	PUBLIC HEALTH ENGLAND (PHE)	Sexual and Reproductive Health Activity Data (SRHAD)	Anonymised - ICO Code Compliant	Sensitive	Health and Social Care Act 2012	Ongoing	Y	The DIDS data contains information on the diagnostic imaging tests, such as x-rays and MRI scans, carried out on cancer patients treated in NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE on the National Cancer Register (https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras). PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis (such as DIDS), treatment and outcomes of these patients. The intelligence generated by cancer registration is used to improve the early detection and treatment of cancer, both by supporting direct patient care (for example, through genetic counselling services) and informing the commissioning and provision of improved diagnostic and treatment services.PHE is only provided with DIDS data for patients on the cancer register. PHE provides NHSD with a file containing NHS Number and the date of birth of cancer patients, which is then used to extract the relevant records from the DIDS system. This linked data is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. PHE has Section 251 support to collect information on all cases of cancer diagnosed in NHS hospitals in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these patients. The HES data linked to cancer registration records is used to produce a range of statistics and analyse trends in the incidence and prevalence of different cancers, and to understand how effective different treatments are in improving patient outcomes. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the CancerData website at www.cancerdata.nhs.uk. The findings derived from the cancer register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the National Cancer Strategy Implementation Plan.The patient identifiable HES data provided by NHSD to PHE is linked to a number of collections of information on patients with infectious diseases to monitor, investigate and manage: a)     antimicrobial resistanceb)    healthcare associated infectionsc)     respiratory diseasesd)    vaccine safetye)     vaccine efficacy and cost effectiveness. PHE is responsible for providing the national infrastructure for health protection including: an integrated surveillance system; specialist services such as diagnostic and reference microbiology; investigation and management of outbreaks of infectious diseases; ensuring effective emergency preparedness, resilience and response for health emergencies, including work on antimicrobial resistance; and evaluating the effectiveness of immunisation programmes and providing vaccines. HES is linked to laboratory data on antimicrobial resistance and healthcare associated infections to analyse the length of time affected patients spend in hospital and what their outcomes are in order to improve the way these threats to public health are managed. It is linked to laboratory data on infectious respiratory diseases to analyse the effectiveness of the treatments provided to patients. It is also linked to vaccination records to identify any health issues associated with new and existing vaccines, and to assess the efficacy and cost effectiveness of vaccines in preventing disease. The findings derived from the linked HES data primarily are used to inform the development and assess the effectiveness of clinical guidelines to improve the control of infections, the management of antimicrobial resistance and the treatment of respiratory diseases. The findings are also used to produce a range of indicators and reports such as the healthcare associated infections data at https://www.gov.uk/government/collections/healthcare-associated-infections-hcai-guidance-data-and-analysis and the vaccination information at https://www.gov.uk/government/collections/vaccine-uptake.The patient identifiable HES data provided by NHSD to PHE is linked to the records of individuals with a congenital anomaly or rare disease (CARD) held by PHE in the National Congenital Anomaly and Rare Diseases Register (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-registration-service-ncardrs). PHE has Section 251 support to collect information on cases of CARDs in England and to link this to other sources of information on the diagnosis, treatment (such as HES) and outcomes of these individuals. This linked data is used to understand more about the incidence and prevalence of different CARDs in England, and about the treatment and outcomes of these individuals. A CARDS annual report will be produced in 2017/18 and prevalence statistics and prenatal detection rates are submitted to the European Surveillance of Congenital Anomalies (EUROCAT) network (http://www.eurocat-network.eu/). The findings derived from the CARDs register data are also used to inform the development and monitor the effectiveness of national policies and initiatives such as the UK Strategy for Rare Diseases.SUS data is essentially an earlier, less ‘clean’ version of the HES data described above (for example, not all duplicate records have been removed) and contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The identifiable patient data provided by NHSD to PHE is linked to the records of patients with cancer held by PHE in the National Cancer Register. Data is only provided by NHSD for patients with a cancer-related ICD-10 diagnosis code. This linked data is used for the specific purpose of producing a set of monthly-updated cancer outcomes metrics on the performance of the NHS in diagnosing and treating cancer patients as soon as possible, such as cancer stage at diagnosis and the percentage of cases diagnosed as an emergency. These metrics, as well as other statistics based on the analysis of data from the cancer register, are published at http://www.ncin.org.uk/cancer_type_and_topic_specific_work/topic_specific_work/cancer_outcome_metrics. The HES data contains information on the diagnosis and treatment of all patients admitted to or attending NHS hospitals in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of a wide range of conditions such as heart disease and stroke, mental health and respiratory disease. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Public Health Outcomes Framework at http://www.phoutcomes.info/. The findings derived from HES data are used to inform the development and monitor the effectiveness of national policies and initiatives aimed at protecting and improving public health and reducing health inequalities. The HES data is also analysed by PHE to produce statistics to help Local Authorities fulfil their statutory duty to improve the health of their local population, for example through the production of joint strategic needs assessments and local health and wellbeing strategies. The MHSDS contains information on the diagnosis and treatment of patients admitted to or attending NHS hospitals and treatment centres with mental health problems in England. The anonymised data provided by NHSD to PHE is used to produce a range of statistics and analyse trends in the incidence and prevalence of mental health problems such as dementia and depression and anxiety. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the Common Mental Health Disorders Profiling Tool at https://fingertips.phe.org.uk/profile-group/mental-health/profile/common-mental-disorders. The findings derived from MHSDS are also used by PHE to develop and assess the effectiveness of national policies aimed at improving the lives of people with mental health problems, and by Local Authorities to provide mental health services at a local level. The NCMP provides information on the height and weight of all children in reception (aged 4-5 years) and year 6 (aged 10-11 years) in schools in England. PHE is the sponsor on behalf of the Secretary of State of the national collection of NCMP data by NHSD from all Local Authorities. The anonymised data provided by NHSD to PHE is used to analyse variations and trends in the percentages of children who are underweight, normal weight, overweight and obese. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority profiles at http://fingertips.phe.org.uk/profile/national-child-measurement-programme. PHE also use the data to provide statistics to schools on the percentage of children who are an unhealthy weight.The NCMP findings are used by PHE to develop national policies to increase the proportion of children who are normal weight, and by Local Authorities to provide services that support families to make healthy lifestyle changes. The SRHAD collects information on the contraception, sexually transmitted infections (STI) and reproductive health advice and care provided to patients attending sexual and reproductive health (SRH) services in England. PHE is the sponsor on behalf of the Secretary of State for the national collection of SRHAD data by NHSD from all SRH services. The anonymised data provided by NHSD to PHE is used to monitor the implementation of key national sexual health policy objectives such as: increasing access to all methods of contraception, including long acting reversible contraception and emergency contraception, and reducing inequalities in access to services; reducing teenage conceptions; and reducing unintended pregnancies. The results of these analyses are published by PHE in a range of indicators, analytical tools and reports such as the local authority sexual and reproductive health profiles at https://fingertips.phe.org.uk/profile/sexualhealth. The SRHAD findings are used by PHE to develop and monitor national policies for Sexual Health and HIV, and by Local Authorities to provide SRH services to improve sexual and reproductive health across England.
REGIONAL DRUG & THERAPEUTIC CENTRE	REGIONAL DRUG & THERAPEUTIC CENTRE	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Prescribing is the most common patient-level intervention in the NHS, and covers all sectors of care: primary, hospital, public and community health. It is the second highest area of spending in the NHS, after staffing costs (NHS Digital). The Regional Drug and Therapeutics Centre at Newcastle (RDTC) hosted by the Newcastle Upon Tyne Foundation Trust (NUTH) extracts and analyses prescribing data to identify trends and variation, and to support medicines optimization. These are presented in reports at regional and CCG level in relation to particular conditions, such as diabetes. Scatter charts incorporating the HES data are included within these reports, and are used to present the outcomes that may be attributed in some part to prescribing patterns within an area. In particular, the reports are useful for CCGs to benchmark against others, identifying where they, for example, have an unusual or high prescribing pattern for a particular condition, which could have a higher than average cost. They can then use this information to investigate further, consider practice in other CCGs, and make changes accordingly, with a view to being more cost effective. Reducing unwarranted variation and increasing value through medicines optimisation is a crucial element of NHS RightCare’s innovation work (further information can be found on the NHS RightCare website). RDTC prescribing reports were developed in response to requests from CCGs (and this work is funded by the CCGs) so that variation in prescribing between CCGs could be illustrated, so that they can benchmark their performance against their peers regionally and nationally. Identifying variation at this level prompts CCGs to investigate the causes of variation locally. They can then identify better performing CCGs and make contact with them to share best practice, which can be implemented locally with an aim to improve prescribing and outcomes in their area. The RDTC produces a series of reports and publications for their stakeholders across the North of England, utilizing EPACT data, Quality Outcomes Framework (QOF) data and for a small number of their charts Hospital Episodes Statistics (HES) data. The RDTC requires HES data for use in the therapeutic prescribing reports, in the attached example the scatter chart illustrates that the CCGs sitting in the bottom left quadrat are demonstrating lower prescribing costs in diabetes realising lower hospital admissions whereas the CCGs sitting in the top right quadrat are demonstrating higher admissions with higher prescribing costs. It would be of benefit for the latter CCGs to communicate with those better performing CCGs to understand steps that can be taken to improve their prescribing and outcome position. The RDTC is hosted by the Newcastle Upon Tyne Foundation Trust (the data controller), however data processing is undertaken by the prescribing reports team within the RDTC. The reports are produced only for RDTC stakeholders as listed: Newcastle Gateshead CCG North Cumbria CCG North Tyneside CCG Northumberland CCG South Tyneside CCG Sunderland CCG Darlington CCG Durham Dales, Easington and Sedgefield CCG Hartlepool and Stockton CCG North Durham CCG South Tees CCG Bolton CCG Bury CCG Heywood, Middleton and Rochdale CCG Manchester CCG Oldham CCG Salford CCG Stockport CCG Tameside and Glossop CCG Trafford CCG Wigan Borough CCG East Riding of Yorkshire CCG Hambleton, Richmondshire and Whitby CCG Harrogate and Rural District CCG Hull CCG North East Lincolnshire CCG North Lincolnshire CCG Scarborough and Ryedale CCG Vale of York CCG Rotherham CCG Airedale, Wharfedale and Craven CCG Bradford City CCG Bradford Districts CCG Leeds North CCG Leeds South and East CCG Leeds West CCG North of England Commissioning Support Service Greater Manchester Shared Service Yorkshire Shared Service HES data is currently only supplied to these users within the form of scatter charts presented at either an area team level or CCG level. This work was instigated in response to stakeholder requests that it would be of value to include information highlighting whether prescribing patterns could influence episodes of hospital admissions. This data is only presented at CCG level, and is only extracted for processing at CCG level. The aim of this work is to highlight variation in prescribing between CCGs, more specifically the use of HES data within these reports aims to highlight possible variation in hospitals admissions due to prescribing practice. It maybe that the data suggests that higher prescribing costs within a therapeutic area by CCG A do not lead to reduced hospital admissions compared to CCG B who is prescribing at a lower cost. National data is specifically required in order to enable benchmarking of CCGs. RDTC were previously granted access to HES data in October 2013 and have been incorporating the data into their reports since October 2014 via the data depot system and the HDIS system. They have had no access to HES data since July 2016 in any form (i.e. HDIS or an extract), hence the request for a new DSA. The reports are updated quarterly, the following data is required for inclusion in the therapeutic reports: Diabetes report • Diabetes hospital admissions (ICD-10 codes E10-E14) per diabetes patients within each CCG compared against spend on diabetic drugs per diabetic patients or QOF target DM007. • Severe hypoglycaemia hospital admissions (ICD-10 codes E160-162) per diabetic patients compared against insulin Determir, Glargine and Degludec as a percentage of all long/inter insulin analogues. Respiratory report • Chronic Obstructive Pulmonary Disease (COPD) hospital admissions (ICD-10 codes J40-J44) per COPD patients compared against spend on LAMA inhalers. • COPD and asthma hospital admissions (ICD-10 codes J40-J45) per COPD and asthma patients compared against spend on bronchodilators and corticosteroids. Cardiovascular report • Stroke hospital admissions (ICD-10 codes I60-I69) with a secondary diagnosis for Atrial Fibrillation (ICD-10 codes I48) per stroke patients compared against defined daily doses (DDDs) per stroke patients. Bone Metabolism report • Hip fracture hospital admissions (ICD-10 codes S720-S722) per bisphosphonate STARPU compared against spend on bisphosphonate drugs per bisphosphonate STARPU. The applicant requires this data from NHS Digital, as it is the only avenue available to them.	Data is extracted from HDIS aggregated at CCG level. Record-level data cannot be downloaded and, in line with the HES Analysis Guide suppression rules, small numbers will be suppressed and/or aggregated, and will not include any description of a cell size below 6. Data is saved within a restricted drive as per the Protocol - RDTC Management of Hospital Episode Statistics (HES) data. The original downloads are password protected. All downloads are recorded in the extraction log including: • date and purpose of extraction. • the name and location of the extraction. • the report the extraction will be included in. • a review date for deletion. Data is imported into a specific database which only holds hospital data aggregates the data to LAT, Region and North of England level. The data then flows via linked tables into databases for each therapeutic area and after processing for weighting with the data’s denominator. The data is then linked to a further database which holds all scatter plot data. Data is copied from this database into a spreadsheet for all scatterplot data and finally copied into the therapeutic report spreadsheet. The reports are made available to stakeholders via the centres website which is password protected. A PDF of the reports dashboard summary is emailed to all stakeholders. Data is only handled as per the protocol and data flow maps. Data will only be accessed by individuals within the prescribing reports team of the prescribing support unit at the RDTC who have authorisation from NHS Digital to access the data for the purpose(s) described, all of whom are substantive employees of the RDTC. Only one team member currently has the authority and thus the log in details to access HDIS system, and will be the only user. HES data will be plotted against EPACT prescribing data and displayed as scatter charts. It will only be presented at CCG and Area Team level. The data will not be linked with any record level data. There will be no requirement nor attempt to reidentify individuals from the data. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. Access is being requested to admitted patient care data as this will provide the Trust with the data required to produce the scatter charts detailed above. They only require data for the current financial year, national data is required in order that The Trust will filter this to only include the exact CCGs they require as not all CCGs nationally fit the top 10 most similar CCGs, for example Central London (Westminster) CCG does not feature in the top 10 similar CCGs of any of the applicant's stakeholders so the data could be filtered out. Data will be filtered by the applicant for specific conditions using ICD-10 codes within the diagnosis fields. For example diabetes admissions counts will be identified using the E10 to E14 diagnosis codes. Processing of HES Accident and Emergency, Outpatient and Critical Care data is not permitted under this agreement. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). this agreement. The following conditions apply to HDIS access: 1) Access to HDIS will be restricted to approved users agreed with the NHS Digital in a controlled manner. Only users who have undergone Information Governance training as required by IG Toolkit v14 may be permitted to access HDIS. 2) Initially, 1 user licences are approved and this will be managed under change control. The charges outlined in the agreement may therefore vary over the agreement period. 3) An annual review of the system use will be completed as part of the audit process. 4) The NHS Digital will monitor use of the HDIS system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. 5) Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. 6) Where any record level data may have been downloaded previously from HDIS, such data must be securely destroyed and certificate of data destruction provided to NHS Digital within 2 months of this agreement. 7) Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. 8) Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. 9) All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	The following outputs will be produced: Therapeutic prescribing reports will be provided to stakeholder CCGs on a quarterly basis via the secure area of the RDTC website which stakeholders can access via password protection. The pdf summary report document is also emailed to stakeholders to alert them to the fact that a new therapeutic report is available from the website. The RDTC twitter account also alerts stakeholders that a new report has been added to the website via a statement such as “the latest RDTC cardiovascular report is now available to stakeholders on the RDTC website”. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Published outputs will not identify individual general practices. CCG medicines optimisation teams use the information within the report to benchmark their prescribing against their neighbouring CCGs and their comparator CCGs. The hospital admissions data enable CCGs to identify whether their prescribing practice is leading to improved outcomes for patients via a reduction in hospital admission for that condition. CCG medicines optimisation teams can also look to those CCGs where there appear to be "better" outcomes and seek to replicate this success. For example identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD, may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). CCGs across GM has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs. The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.	By highlighting to medicines optimisation teams any potential relationship between prescribing patterns and hospital admissions organisations can work to identify ways to optimize prescribing, enabling the most cost effective use of medicines across the health economy. Identifying better outcomes to the patient population by a change in prescribing pattern e.g. identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD , may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). One of the applicant's stakeholder regions has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.
REGIONAL DRUG & THERAPEUTIC CENTRE	REGIONAL DRUG & THERAPEUTIC CENTRE	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Prescribing is the most common patient-level intervention in the NHS, and covers all sectors of care: primary, hospital, public and community health. It is the second highest area of spending in the NHS, after staffing costs (NHS Digital). The Regional Drug and Therapeutics Centre at Newcastle (RDTC) hosted by the Newcastle Upon Tyne Foundation Trust (NUTH) extracts and analyses prescribing data to identify trends and variation, and to support medicines optimization. These are presented in reports at regional and CCG level in relation to particular conditions, such as diabetes. Scatter charts incorporating the HES data are included within these reports, and are used to present the outcomes that may be attributed in some part to prescribing patterns within an area. In particular, the reports are useful for CCGs to benchmark against others, identifying where they, for example, have an unusual or high prescribing pattern for a particular condition, which could have a higher than average cost. They can then use this information to investigate further, consider practice in other CCGs, and make changes accordingly, with a view to being more cost effective. Reducing unwarranted variation and increasing value through medicines optimisation is a crucial element of NHS RightCare’s innovation work (further information can be found on the NHS RightCare website). RDTC prescribing reports were developed in response to requests from CCGs (and this work is funded by the CCGs) so that variation in prescribing between CCGs could be illustrated, so that they can benchmark their performance against their peers regionally and nationally. Identifying variation at this level prompts CCGs to investigate the causes of variation locally. They can then identify better performing CCGs and make contact with them to share best practice, which can be implemented locally with an aim to improve prescribing and outcomes in their area. The RDTC produces a series of reports and publications for their stakeholders across the North of England, utilizing EPACT data, Quality Outcomes Framework (QOF) data and for a small number of their charts Hospital Episodes Statistics (HES) data. The RDTC requires HES data for use in the therapeutic prescribing reports, in the attached example the scatter chart illustrates that the CCGs sitting in the bottom left quadrat are demonstrating lower prescribing costs in diabetes realising lower hospital admissions whereas the CCGs sitting in the top right quadrat are demonstrating higher admissions with higher prescribing costs. It would be of benefit for the latter CCGs to communicate with those better performing CCGs to understand steps that can be taken to improve their prescribing and outcome position. The RDTC is hosted by the Newcastle Upon Tyne Foundation Trust (the data controller), however data processing is undertaken by the prescribing reports team within the RDTC. The reports are produced only for RDTC stakeholders as listed: Newcastle Gateshead CCG North Cumbria CCG North Tyneside CCG Northumberland CCG South Tyneside CCG Sunderland CCG Darlington CCG Durham Dales, Easington and Sedgefield CCG Hartlepool and Stockton CCG North Durham CCG South Tees CCG Bolton CCG Bury CCG Heywood, Middleton and Rochdale CCG Manchester CCG Oldham CCG Salford CCG Stockport CCG Tameside and Glossop CCG Trafford CCG Wigan Borough CCG East Riding of Yorkshire CCG Hambleton, Richmondshire and Whitby CCG Harrogate and Rural District CCG Hull CCG North East Lincolnshire CCG North Lincolnshire CCG Scarborough and Ryedale CCG Vale of York CCG Rotherham CCG Airedale, Wharfedale and Craven CCG Bradford City CCG Bradford Districts CCG Leeds North CCG Leeds South and East CCG Leeds West CCG North of England Commissioning Support Service Greater Manchester Shared Service Yorkshire Shared Service HES data is currently only supplied to these users within the form of scatter charts presented at either an area team level or CCG level. This work was instigated in response to stakeholder requests that it would be of value to include information highlighting whether prescribing patterns could influence episodes of hospital admissions. This data is only presented at CCG level, and is only extracted for processing at CCG level. The aim of this work is to highlight variation in prescribing between CCGs, more specifically the use of HES data within these reports aims to highlight possible variation in hospitals admissions due to prescribing practice. It maybe that the data suggests that higher prescribing costs within a therapeutic area by CCG A do not lead to reduced hospital admissions compared to CCG B who is prescribing at a lower cost. National data is specifically required in order to enable benchmarking of CCGs. RDTC were previously granted access to HES data in October 2013 and have been incorporating the data into their reports since October 2014 via the data depot system and the HDIS system. They have had no access to HES data since July 2016 in any form (i.e. HDIS or an extract), hence the request for a new DSA. The reports are updated quarterly, the following data is required for inclusion in the therapeutic reports: Diabetes report • Diabetes hospital admissions (ICD-10 codes E10-E14) per diabetes patients within each CCG compared against spend on diabetic drugs per diabetic patients or QOF target DM007. • Severe hypoglycaemia hospital admissions (ICD-10 codes E160-162) per diabetic patients compared against insulin Determir, Glargine and Degludec as a percentage of all long/inter insulin analogues. Respiratory report • Chronic Obstructive Pulmonary Disease (COPD) hospital admissions (ICD-10 codes J40-J44) per COPD patients compared against spend on LAMA inhalers. • COPD and asthma hospital admissions (ICD-10 codes J40-J45) per COPD and asthma patients compared against spend on bronchodilators and corticosteroids. Cardiovascular report • Stroke hospital admissions (ICD-10 codes I60-I69) with a secondary diagnosis for Atrial Fibrillation (ICD-10 codes I48) per stroke patients compared against defined daily doses (DDDs) per stroke patients. Bone Metabolism report • Hip fracture hospital admissions (ICD-10 codes S720-S722) per bisphosphonate STARPU compared against spend on bisphosphonate drugs per bisphosphonate STARPU. The applicant requires this data from NHS Digital, as it is the only avenue available to them.	Data is extracted from HDIS aggregated at CCG level. Record-level data cannot be downloaded and, in line with the HES Analysis Guide suppression rules, small numbers will be suppressed and/or aggregated, and will not include any description of a cell size below 6. Data is saved within a restricted drive as per the Protocol - RDTC Management of Hospital Episode Statistics (HES) data. The original downloads are password protected. All downloads are recorded in the extraction log including: • date and purpose of extraction. • the name and location of the extraction. • the report the extraction will be included in. • a review date for deletion. Data is imported into a specific database which only holds hospital data aggregates the data to LAT, Region and North of England level. The data then flows via linked tables into databases for each therapeutic area and after processing for weighting with the data’s denominator. The data is then linked to a further database which holds all scatter plot data. Data is copied from this database into a spreadsheet for all scatterplot data and finally copied into the therapeutic report spreadsheet. The reports are made available to stakeholders via the centres website which is password protected. A PDF of the reports dashboard summary is emailed to all stakeholders. Data is only handled as per the protocol and data flow maps. Data will only be accessed by individuals within the prescribing reports team of the prescribing support unit at the RDTC who have authorisation from NHS Digital to access the data for the purpose(s) described, all of whom are substantive employees of the RDTC. Only one team member currently has the authority and thus the log in details to access HDIS system, and will be the only user. HES data will be plotted against EPACT prescribing data and displayed as scatter charts. It will only be presented at CCG and Area Team level. The data will not be linked with any record level data. There will be no requirement nor attempt to reidentify individuals from the data. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. Access is being requested to admitted patient care data as this will provide the Trust with the data required to produce the scatter charts detailed above. They only require data for the current financial year, national data is required in order that The Trust will filter this to only include the exact CCGs they require as not all CCGs nationally fit the top 10 most similar CCGs, for example Central London (Westminster) CCG does not feature in the top 10 similar CCGs of any of the applicant's stakeholders so the data could be filtered out. Data will be filtered by the applicant for specific conditions using ICD-10 codes within the diagnosis fields. For example diabetes admissions counts will be identified using the E10 to E14 diagnosis codes. Processing of HES Accident and Emergency, Outpatient and Critical Care data is not permitted under this agreement. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). this agreement. The following conditions apply to HDIS access: 1) Access to HDIS will be restricted to approved users agreed with the NHS Digital in a controlled manner. Only users who have undergone Information Governance training as required by IG Toolkit v14 may be permitted to access HDIS. 2) Initially, 1 user licences are approved and this will be managed under change control. The charges outlined in the agreement may therefore vary over the agreement period. 3) An annual review of the system use will be completed as part of the audit process. 4) The NHS Digital will monitor use of the HDIS system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. 5) Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. 6) Where any record level data may have been downloaded previously from HDIS, such data must be securely destroyed and certificate of data destruction provided to NHS Digital within 2 months of this agreement. 7) Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. 8) Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. 9) All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	The following outputs will be produced: Therapeutic prescribing reports will be provided to stakeholder CCGs on a quarterly basis via the secure area of the RDTC website which stakeholders can access via password protection. The pdf summary report document is also emailed to stakeholders to alert them to the fact that a new therapeutic report is available from the website. The RDTC twitter account also alerts stakeholders that a new report has been added to the website via a statement such as “the latest RDTC cardiovascular report is now available to stakeholders on the RDTC website”. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Published outputs will not identify individual general practices. CCG medicines optimisation teams use the information within the report to benchmark their prescribing against their neighbouring CCGs and their comparator CCGs. The hospital admissions data enable CCGs to identify whether their prescribing practice is leading to improved outcomes for patients via a reduction in hospital admission for that condition. CCG medicines optimisation teams can also look to those CCGs where there appear to be "better" outcomes and seek to replicate this success. For example identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD, may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). CCGs across GM has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs. The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.	By highlighting to medicines optimisation teams any potential relationship between prescribing patterns and hospital admissions organisations can work to identify ways to optimize prescribing, enabling the most cost effective use of medicines across the health economy. Identifying better outcomes to the patient population by a change in prescribing pattern e.g. identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD , may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). One of the applicant's stakeholder regions has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.
REGIONAL DRUG & THERAPEUTIC CENTRE	REGIONAL DRUG & THERAPEUTIC CENTRE	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Prescribing is the most common patient-level intervention in the NHS, and covers all sectors of care: primary, hospital, public and community health. It is the second highest area of spending in the NHS, after staffing costs (NHS Digital). The Regional Drug and Therapeutics Centre at Newcastle (RDTC) hosted by the Newcastle Upon Tyne Foundation Trust (NUTH) extracts and analyses prescribing data to identify trends and variation, and to support medicines optimization. These are presented in reports at regional and CCG level in relation to particular conditions, such as diabetes. Scatter charts incorporating the HES data are included within these reports, and are used to present the outcomes that may be attributed in some part to prescribing patterns within an area. In particular, the reports are useful for CCGs to benchmark against others, identifying where they, for example, have an unusual or high prescribing pattern for a particular condition, which could have a higher than average cost. They can then use this information to investigate further, consider practice in other CCGs, and make changes accordingly, with a view to being more cost effective. Reducing unwarranted variation and increasing value through medicines optimisation is a crucial element of NHS RightCare’s innovation work (further information can be found on the NHS RightCare website). RDTC prescribing reports were developed in response to requests from CCGs (and this work is funded by the CCGs) so that variation in prescribing between CCGs could be illustrated, so that they can benchmark their performance against their peers regionally and nationally. Identifying variation at this level prompts CCGs to investigate the causes of variation locally. They can then identify better performing CCGs and make contact with them to share best practice, which can be implemented locally with an aim to improve prescribing and outcomes in their area. The RDTC produces a series of reports and publications for their stakeholders across the North of England, utilizing EPACT data, Quality Outcomes Framework (QOF) data and for a small number of their charts Hospital Episodes Statistics (HES) data. The RDTC requires HES data for use in the therapeutic prescribing reports, in the attached example the scatter chart illustrates that the CCGs sitting in the bottom left quadrat are demonstrating lower prescribing costs in diabetes realising lower hospital admissions whereas the CCGs sitting in the top right quadrat are demonstrating higher admissions with higher prescribing costs. It would be of benefit for the latter CCGs to communicate with those better performing CCGs to understand steps that can be taken to improve their prescribing and outcome position. The RDTC is hosted by the Newcastle Upon Tyne Foundation Trust (the data controller), however data processing is undertaken by the prescribing reports team within the RDTC. The reports are produced only for RDTC stakeholders as listed: Newcastle Gateshead CCG North Cumbria CCG North Tyneside CCG Northumberland CCG South Tyneside CCG Sunderland CCG Darlington CCG Durham Dales, Easington and Sedgefield CCG Hartlepool and Stockton CCG North Durham CCG South Tees CCG Bolton CCG Bury CCG Heywood, Middleton and Rochdale CCG Manchester CCG Oldham CCG Salford CCG Stockport CCG Tameside and Glossop CCG Trafford CCG Wigan Borough CCG East Riding of Yorkshire CCG Hambleton, Richmondshire and Whitby CCG Harrogate and Rural District CCG Hull CCG North East Lincolnshire CCG North Lincolnshire CCG Scarborough and Ryedale CCG Vale of York CCG Rotherham CCG Airedale, Wharfedale and Craven CCG Bradford City CCG Bradford Districts CCG Leeds North CCG Leeds South and East CCG Leeds West CCG North of England Commissioning Support Service Greater Manchester Shared Service Yorkshire Shared Service HES data is currently only supplied to these users within the form of scatter charts presented at either an area team level or CCG level. This work was instigated in response to stakeholder requests that it would be of value to include information highlighting whether prescribing patterns could influence episodes of hospital admissions. This data is only presented at CCG level, and is only extracted for processing at CCG level. The aim of this work is to highlight variation in prescribing between CCGs, more specifically the use of HES data within these reports aims to highlight possible variation in hospitals admissions due to prescribing practice. It maybe that the data suggests that higher prescribing costs within a therapeutic area by CCG A do not lead to reduced hospital admissions compared to CCG B who is prescribing at a lower cost. National data is specifically required in order to enable benchmarking of CCGs. RDTC were previously granted access to HES data in October 2013 and have been incorporating the data into their reports since October 2014 via the data depot system and the HDIS system. They have had no access to HES data since July 2016 in any form (i.e. HDIS or an extract), hence the request for a new DSA. The reports are updated quarterly, the following data is required for inclusion in the therapeutic reports: Diabetes report • Diabetes hospital admissions (ICD-10 codes E10-E14) per diabetes patients within each CCG compared against spend on diabetic drugs per diabetic patients or QOF target DM007. • Severe hypoglycaemia hospital admissions (ICD-10 codes E160-162) per diabetic patients compared against insulin Determir, Glargine and Degludec as a percentage of all long/inter insulin analogues. Respiratory report • Chronic Obstructive Pulmonary Disease (COPD) hospital admissions (ICD-10 codes J40-J44) per COPD patients compared against spend on LAMA inhalers. • COPD and asthma hospital admissions (ICD-10 codes J40-J45) per COPD and asthma patients compared against spend on bronchodilators and corticosteroids. Cardiovascular report • Stroke hospital admissions (ICD-10 codes I60-I69) with a secondary diagnosis for Atrial Fibrillation (ICD-10 codes I48) per stroke patients compared against defined daily doses (DDDs) per stroke patients. Bone Metabolism report • Hip fracture hospital admissions (ICD-10 codes S720-S722) per bisphosphonate STARPU compared against spend on bisphosphonate drugs per bisphosphonate STARPU. The applicant requires this data from NHS Digital, as it is the only avenue available to them.	Data is extracted from HDIS aggregated at CCG level. Record-level data cannot be downloaded and, in line with the HES Analysis Guide suppression rules, small numbers will be suppressed and/or aggregated, and will not include any description of a cell size below 6. Data is saved within a restricted drive as per the Protocol - RDTC Management of Hospital Episode Statistics (HES) data. The original downloads are password protected. All downloads are recorded in the extraction log including: • date and purpose of extraction. • the name and location of the extraction. • the report the extraction will be included in. • a review date for deletion. Data is imported into a specific database which only holds hospital data aggregates the data to LAT, Region and North of England level. The data then flows via linked tables into databases for each therapeutic area and after processing for weighting with the data’s denominator. The data is then linked to a further database which holds all scatter plot data. Data is copied from this database into a spreadsheet for all scatterplot data and finally copied into the therapeutic report spreadsheet. The reports are made available to stakeholders via the centres website which is password protected. A PDF of the reports dashboard summary is emailed to all stakeholders. Data is only handled as per the protocol and data flow maps. Data will only be accessed by individuals within the prescribing reports team of the prescribing support unit at the RDTC who have authorisation from NHS Digital to access the data for the purpose(s) described, all of whom are substantive employees of the RDTC. Only one team member currently has the authority and thus the log in details to access HDIS system, and will be the only user. HES data will be plotted against EPACT prescribing data and displayed as scatter charts. It will only be presented at CCG and Area Team level. The data will not be linked with any record level data. There will be no requirement nor attempt to reidentify individuals from the data. The data will not be made available to any third parties other than those specified except in the form of aggregated outputs with small numbers suppressed in line with the HES Analysis Guide. Access is being requested to admitted patient care data as this will provide the Trust with the data required to produce the scatter charts detailed above. They only require data for the current financial year, national data is required in order that The Trust will filter this to only include the exact CCGs they require as not all CCGs nationally fit the top 10 most similar CCGs, for example Central London (Westminster) CCG does not feature in the top 10 similar CCGs of any of the applicant's stakeholders so the data could be filtered out. Data will be filtered by the applicant for specific conditions using ICD-10 codes within the diagnosis fields. For example diabetes admissions counts will be identified using the E10 to E14 diagnosis codes. Processing of HES Accident and Emergency, Outpatient and Critical Care data is not permitted under this agreement. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data). this agreement. The following conditions apply to HDIS access: 1) Access to HDIS will be restricted to approved users agreed with the NHS Digital in a controlled manner. Only users who have undergone Information Governance training as required by IG Toolkit v14 may be permitted to access HDIS. 2) Initially, 1 user licences are approved and this will be managed under change control. The charges outlined in the agreement may therefore vary over the agreement period. 3) An annual review of the system use will be completed as part of the audit process. 4) The NHS Digital will monitor use of the HDIS system as part of ongoing access and any excessive use will be reviewed and access could be withdrawn with data destruction notices issued if that occurs. 5) Users are only permitted to download tabulated data (which may contain small numbers) from the system. Downloading of record-level data or record level linkage is not permitted under this agreement. 6) Where any record level data may have been downloaded previously from HDIS, such data must be securely destroyed and certificate of data destruction provided to NHS Digital within 2 months of this agreement. 7) Where downloaded aggregated data contains small numbers, such data must be securely destroyed at the end of the data sharing agreement, and a certificate of data destruction supplied to NHS Digital. 8) Where downloaded aggregated data is suppressed in line with the HES analysis guide, such data may be retained beyond the period of this agreement. 9) All outputs shared by the licensee must have small numbers suppressed in line with the HES analysis guide.	The following outputs will be produced: Therapeutic prescribing reports will be provided to stakeholder CCGs on a quarterly basis via the secure area of the RDTC website which stakeholders can access via password protection. The pdf summary report document is also emailed to stakeholders to alert them to the fact that a new therapeutic report is available from the website. The RDTC twitter account also alerts stakeholders that a new report has been added to the website via a statement such as “the latest RDTC cardiovascular report is now available to stakeholders on the RDTC website”. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Published outputs will not identify individual general practices. CCG medicines optimisation teams use the information within the report to benchmark their prescribing against their neighbouring CCGs and their comparator CCGs. The hospital admissions data enable CCGs to identify whether their prescribing practice is leading to improved outcomes for patients via a reduction in hospital admission for that condition. CCG medicines optimisation teams can also look to those CCGs where there appear to be "better" outcomes and seek to replicate this success. For example identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD, may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). CCGs across GM has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs. The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.	By highlighting to medicines optimisation teams any potential relationship between prescribing patterns and hospital admissions organisations can work to identify ways to optimize prescribing, enabling the most cost effective use of medicines across the health economy. Identifying better outcomes to the patient population by a change in prescribing pattern e.g. identifying that lower prescribing rates of high dose inhaled corticosteroids (ICS) does not result in increased hospital admissions for an exacerbation of COPD , may support prescribers to reduce high dose ICS prescribing which is of health benefit to the patient. High dose ICS are associated with an increased risk of systemic side effects, including adrenal suppression and growth retardation in children (NICE Academic detailing aid, July 2012). One of the applicant's stakeholder regions has implemented a new treatment pathway across its health economy to address the following issues: • Multitude of different inhalers and inhaler types • Probable overprescribing of inhaled corticosteroids • Variation between CCGs in admission rates and spend on respiratory drugs The outputs being measured are: • Any change in corticosteroid prescribing • Any change in exacerbations of COPD • Any change in COPD referral or admission rates. Using the RDTC respiratory report the group are able to watch for any change in trends of ICS prescribing and also any change in hospital admissions for COPD exacerbations. If a correlation is identified then the team can investigate further using their local data. The benefit of using CCG reports enables the stakeholder to benchmark their progress against other CCGs, where the health economy is working on one footprint such as in Greater Manchester this enables the medicines management group to consider the whole health economy whilst being able to instigate variation at CCG level within that health economy.
ROYAL COLLEGE OF ANAESTHETISTS	ROYAL COLLEGE OF ANAESTHETISTS	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Emergency abdominal surgery (or emergency laparotomy) is associated with significant morbidity and mortality worldwide. The aim of The National Emergency Laparotomy Audit (NELA) is to enable the improvement of the quality of care of patients undergoing emergency laparotomy by providing high quality comparative information of the clinical practice and outcomes of all NHS providers of emergency laparotomy in England and Wales. NELA is a national clinical audit commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical and Patient Outcomes Programme (NCAPOP). HQIP have commissioned the Royal College of Anaesthetists (RCoA) to deliver the audit. RCoA are working in partnership with the Clinical Effectiveness Unit of the Royal College of Surgeons (RCS) and therefore make up the NELA project team. HQIP act as data controllers for the national clinical audit but do not have access to any data collected or analysed by the RCoA and RCS who are the data processors. The analysis by the NELA project team will only involve pseudonymised (with exception to full Date of Death and Cause of Death) datasets that combine information submitted by NHS hospitals and data supplied by NHS Digital / ONS. Members of the project team work at either the RCoA or Clinical Effectiveness Unit at the RCS, however analysis of the NHS Digital, ONS, and an extract of pseudonymised NELA data (including full Date of Death obtained via hospitals) will only be processed and stored at RCS. The RCoA and RCS wish to link the patient records submitted to NELA with the Hospital Episode Statistics (HES) records for those patients. The NELA records relate only to an individual admission, and by linking to inpatient HES data, the Audit will be able to provide more precise and relevant information to NHS hospitals by allowing the RCoA and RCS to describe longer term outcomes (e.g., readmission rates) and to improve RCoA’s and RCS’s risk-adjustment models by using the extensive information on comorbid conditions held within HES. (eg to calculate the Charlson Comorbidity score) The RCoA and RCS also wish to link the patient records submitted to NELA with the Office for National Statistics (ONS) Death Register on a quarterly basis to enable the Audit to monitor changes in postoperative outcomes (both short and longer-term mortality) for those patients. Access to this linked information will support this national clinical audit to improve the quality of care within NHS hospitals for a high-risk patient group. The objectives of the Audit are: 1. To enable secondary care providers to improve the delivery of care to patients undergoing emergency laparotomy using information produced by the audit; 2. To provide comparative information on the organisation of care by providers of Emergency Laparotomy. 3. To provide comparative information on patient outcomes following surgery for Emergency Laparotomy. 4. To facilitate the development of effective change (quality improvement) initiatives and thereby spread examples of best practice and help local providers make the best possible use of audit results In summary, the purpose of this request is to support national clinical audit, quality improvement within hospital, and research on methods to monitor surgical outcomes. The NELA team is planning to request patient level clinical data from Intensive Care National Audit and Research Centre (ICNARC).	The RCoA are the principal data processors for NELA and manage the extraction of the records from the NELA IT system. RCoA will send the file of patient identifiers and the NELA ID to NHS Digital for linkage to HES and ONS fields. Pseudonymised files (of which includes full date of death and Cause of Death (text)) from NHS Digital will contain the HES and ONS fields with the NELA ID variable added. The pseudonymised files of HES / ONS data (including Date of Death and Cause of Death) will be received by the RCS and held on their secure data server. In all cases, the data received from NHS Digital will not be linked back to the identifiable NELA database held at RCoA. An extract of pseudonymised data of which includes date of death is taken from the NELA database and sent to RCS. This data will be linked to the HES-ONS data via the NELA ID. Date of Death from the NELA database is provided as this includes an important data quality step. There are potential missed linkages if RCS and RCoA do not have this information when processing the data at RCS. This also helps to validate the data entered into NELA. No data provided by NHS Digital is sent to RCoA to correct fields in the NELA database. A copy of the de-identified data fields along with the unique NELA ID will be analysed by project team members from both RCS and RCoA at the RCS Clinical Effectiveness Unit only. The only identifiable fields received by RCS and RCoA analysts are ONS Date of Death and Cause of death. The full Date of Death is required to be able to calculate survival at multiple time points (30 day, 90 day, etc.). There will be no capacity for RCS / RCoA analysts to use the ONS Date of Death to identify any individual patients. Analysts from RCS / RCoA who work on the de-identified data set do not have access to the identifiable data set held within the NELA IT system and managed by the RCoA, nor the list of patient identifiers sent to NHS digital for linkage purposes. All individuals with access to the de-identified data are substantively employed by RCoA or RCS. The list of patient identifiers sent to NHS digital is only accessible by a senior member of the RCoA. RCoA or RCS will not be linking HES/ONS data with any other dataset (apart from an extract of NELA). Linkage with any other datasets would be subject to a future application and would be supported by an appropriate legal basis. The majority of the analysis involving the de-identified linked patient dataset will be conducted by the RCS statisticians who form part of the NELA Project Team. The remainder would consist of statisticians from RCoA would be involved in some of the analysis of the patient-level dataset and will be located at the RCS Clinical Effectiveness Unit to undertake this work. In either case, all individuals with access to the data are substantively employed by either RCoA, or RCS and are required to sign a Confidentiality Agreement before access is granted.	The linked dataset will be a product of this process and will enhance the quality of the comparative data for the audit in subsequent years. The NELA is commissioned to produce a "State of the Nation" annual report each year. The first 2 reports utilising patient level information were published in June 2015 & June 2016 and are available to view on the NELA website. Subsequent reports are scheduled to be published yearly. In order to more widely disseminate the findings of the audit, additional scientific publications will be produced. These outputs will be in the form of peer-review articles and conference presentations. The results of the audit will also be disseminated at professional medical conferences and in peer-reviewed journals (e.g. BMJ, BJA, ASGBI journal, AAGBI journal) at the time of the launch of the report or shortly after. Publications related to the Audit methods (e.g., a risk-adjustment model) rather than information of clinical practice and outcomes will be published on an ad hoc basis. In response to participant feedback a Quality Improvement Report Dashboard has been created on the NELA Online Web Tool to assist sites with audit data collection and to promote local Quality Improvement work (Local hospital data can only be viewed by registered local hospital participants). The Quality Improvement Report Dashboard will only provide local units with aggregated information to compare their performance against a national average. The figures available to each unit will be based on their own local data (supplied by the units) and the Dashboard may therefore present small numbers on some occasions. Each individual user has their own login to the webtool which gives them access to only their own hospital local data. To access the webtool they require a username and password. The first phase of the Dashboard which has now been launched focuses on Case Ascertainment and Patient Demographics. The Patient Demographics section allows local participants to view some basic information on their hospital's population of patients undergoing emergency laparotomy, and how it compares to the audit-wide average. It focuses on characteristics such as patient age and operative urgency. The Case Ascertainment aims at increasing case completeness and submission by providing a monthly list of cases entered/completed/not completed. The next phase of the Dashboard will focus much more on Quality Improvement, feeding back key QI indicators and comparing hospital's local data with national audit-wide averages. Some of the measures reported back will include; Documentation of risk, Direct Admission to critical care etc. The Royal College of Anaesthetists are currently in the process of developing the QI dashboard and hope to make further additions in the next few months. All outputs will be aggregated with small numbers suppressed and will follow the ONS/HES guide on reporting.	The NELA audit is highly relevant to current clinical practice and publications will allow widespread disseminate of the findings amongst health professionals. Linkage to the HES/ONS data allows the Audit to report more extensively on patterns of care beyond the initial hospital admission and longer-term outcomes, such as 90-day mortality. The Audit is able to examine issues such as readmission rates and the most common reasons for these post-discharge complications, e.g.: respiratory complications and anastomotic leaks. The audit will produce useful indicators that describe the standard of care in a variety of clinical areas. The indicators will identify NHS providers that are performing well and those requiring improvement to the quality of care received by patients. Ongoing improvement in the processes of care and clinical outcomes should lead to a reduction in the postoperative mortality rates and thus an overall improvement in patient outcomes. Outcomes will be measured by re-auditing individual sites and therefore regular data linkage would be required. It is hoped that this improvement in care would be identified by the end of the currently proposed commissioned audit period (Dec 2018). The intended audience for the audit annual reports are clinicians, healthcare professionals, Medical Directors, Chief Executives, audit managers, commissioners, NHS England, public and patients. Trusts will use the process indicators and outcomes reported in the annual reports to assess their care against national standards and benchmark against other NHS trusts. This will enable providers to identify areas requiring improvement and take action which in turn will provide a benefit to patient care. Reporting will identify whether NHS trusts are meeting national guidance such as NICE recommendations and will identify variations in the provision of care. The Audit outcomes such as postoperative mortality are risk-adjusted and any potential outlying trusts are identified as part of the Audit outlier policy. Any Trust showing as an alarm will be notified which will allow for investigation into the cause; this can be attributable to either data quality issues or clinical practice. This notification will enable the trust to address the cause and either review the data submitted to the Audit or their clinical practice. Any resultant improvements in clinical practice will directly impact on improvements in patient care. The trust profiles are publically available, providing transparency and enabling patient choice. Publishing in peer-reviewed journals will allow greater discussion of the strengths and weaknesses of the results, and will provide the benefit of peer-review of the work from third parties. It is anticipated that the reports produced as a result of the audit will contribute to clinical guidance and national policy.
ROYAL COLLEGE OF ANAESTHETISTS	ROYAL COLLEGE OF ANAESTHETISTS	MRIS - Cause of Death Report	Identifiable	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	Ongoing	Y	Emergency abdominal surgery (or emergency laparotomy) is associated with significant morbidity and mortality worldwide. The aim of The National Emergency Laparotomy Audit (NELA) is to enable the improvement of the quality of care of patients undergoing emergency laparotomy by providing high quality comparative information of the clinical practice and outcomes of all NHS providers of emergency laparotomy in England and Wales. NELA is a national clinical audit commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical and Patient Outcomes Programme (NCAPOP). HQIP have commissioned the Royal College of Anaesthetists (RCoA) to deliver the audit. RCoA are working in partnership with the Clinical Effectiveness Unit of the Royal College of Surgeons (RCS) and therefore make up the NELA project team. HQIP act as data controllers for the national clinical audit but do not have access to any data collected or analysed by the RCoA and RCS who are the data processors. The analysis by the NELA project team will only involve pseudonymised (with exception to full Date of Death and Cause of Death) datasets that combine information submitted by NHS hospitals and data supplied by NHS Digital / ONS. Members of the project team work at either the RCoA or Clinical Effectiveness Unit at the RCS, however analysis of the NHS Digital, ONS, and an extract of pseudonymised NELA data (including full Date of Death obtained via hospitals) will only be processed and stored at RCS. The RCoA and RCS wish to link the patient records submitted to NELA with the Hospital Episode Statistics (HES) records for those patients. The NELA records relate only to an individual admission, and by linking to inpatient HES data, the Audit will be able to provide more precise and relevant information to NHS hospitals by allowing the RCoA and RCS to describe longer term outcomes (e.g., readmission rates) and to improve RCoA’s and RCS’s risk-adjustment models by using the extensive information on comorbid conditions held within HES. (eg to calculate the Charlson Comorbidity score) The RCoA and RCS also wish to link the patient records submitted to NELA with the Office for National Statistics (ONS) Death Register on a quarterly basis to enable the Audit to monitor changes in postoperative outcomes (both short and longer-term mortality) for those patients. Access to this linked information will support this national clinical audit to improve the quality of care within NHS hospitals for a high-risk patient group. The objectives of the Audit are: 1. To enable secondary care providers to improve the delivery of care to patients undergoing emergency laparotomy using information produced by the audit; 2. To provide comparative information on the organisation of care by providers of Emergency Laparotomy. 3. To provide comparative information on patient outcomes following surgery for Emergency Laparotomy. 4. To facilitate the development of effective change (quality improvement) initiatives and thereby spread examples of best practice and help local providers make the best possible use of audit results In summary, the purpose of this request is to support national clinical audit, quality improvement within hospital, and research on methods to monitor surgical outcomes. The NELA team is planning to request patient level clinical data from Intensive Care National Audit and Research Centre (ICNARC).	The RCoA are the principal data processors for NELA and manage the extraction of the records from the NELA IT system. RCoA will send the file of patient identifiers and the NELA ID to NHS Digital for linkage to HES and ONS fields. Pseudonymised files (of which includes full date of death and Cause of Death (text)) from NHS Digital will contain the HES and ONS fields with the NELA ID variable added. The pseudonymised files of HES / ONS data (including Date of Death and Cause of Death) will be received by the RCS and held on their secure data server. In all cases, the data received from NHS Digital will not be linked back to the identifiable NELA database held at RCoA. An extract of pseudonymised data of which includes date of death is taken from the NELA database and sent to RCS. This data will be linked to the HES-ONS data via the NELA ID. Date of Death from the NELA database is provided as this includes an important data quality step. There are potential missed linkages if RCS and RCoA do not have this information when processing the data at RCS. This also helps to validate the data entered into NELA. No data provided by NHS Digital is sent to RCoA to correct fields in the NELA database. A copy of the de-identified data fields along with the unique NELA ID will be analysed by project team members from both RCS and RCoA at the RCS Clinical Effectiveness Unit only. The only identifiable fields received by RCS and RCoA analysts are ONS Date of Death and Cause of death. The full Date of Death is required to be able to calculate survival at multiple time points (30 day, 90 day, etc.). There will be no capacity for RCS / RCoA analysts to use the ONS Date of Death to identify any individual patients. Analysts from RCS / RCoA who work on the de-identified data set do not have access to the identifiable data set held within the NELA IT system and managed by the RCoA, nor the list of patient identifiers sent to NHS digital for linkage purposes. All individuals with access to the de-identified data are substantively employed by RCoA or RCS. The list of patient identifiers sent to NHS digital is only accessible by a senior member of the RCoA. RCoA or RCS will not be linking HES/ONS data with any other dataset (apart from an extract of NELA). Linkage with any other datasets would be subject to a future application and would be supported by an appropriate legal basis. The majority of the analysis involving the de-identified linked patient dataset will be conducted by the RCS statisticians who form part of the NELA Project Team. The remainder would consist of statisticians from RCoA would be involved in some of the analysis of the patient-level dataset and will be located at the RCS Clinical Effectiveness Unit to undertake this work. In either case, all individuals with access to the data are substantively employed by either RCoA, or RCS and are required to sign a Confidentiality Agreement before access is granted.	The linked dataset will be a product of this process and will enhance the quality of the comparative data for the audit in subsequent years. The NELA is commissioned to produce a "State of the Nation" annual report each year. The first 2 reports utilising patient level information were published in June 2015 & June 2016 and are available to view on the NELA website. Subsequent reports are scheduled to be published yearly. In order to more widely disseminate the findings of the audit, additional scientific publications will be produced. These outputs will be in the form of peer-review articles and conference presentations. The results of the audit will also be disseminated at professional medical conferences and in peer-reviewed journals (e.g. BMJ, BJA, ASGBI journal, AAGBI journal) at the time of the launch of the report or shortly after. Publications related to the Audit methods (e.g., a risk-adjustment model) rather than information of clinical practice and outcomes will be published on an ad hoc basis. In response to participant feedback a Quality Improvement Report Dashboard has been created on the NELA Online Web Tool to assist sites with audit data collection and to promote local Quality Improvement work (Local hospital data can only be viewed by registered local hospital participants). The Quality Improvement Report Dashboard will only provide local units with aggregated information to compare their performance against a national average. The figures available to each unit will be based on their own local data (supplied by the units) and the Dashboard may therefore present small numbers on some occasions. Each individual user has their own login to the webtool which gives them access to only their own hospital local data. To access the webtool they require a username and password. The first phase of the Dashboard which has now been launched focuses on Case Ascertainment and Patient Demographics. The Patient Demographics section allows local participants to view some basic information on their hospital's population of patients undergoing emergency laparotomy, and how it compares to the audit-wide average. It focuses on characteristics such as patient age and operative urgency. The Case Ascertainment aims at increasing case completeness and submission by providing a monthly list of cases entered/completed/not completed. The next phase of the Dashboard will focus much more on Quality Improvement, feeding back key QI indicators and comparing hospital's local data with national audit-wide averages. Some of the measures reported back will include; Documentation of risk, Direct Admission to critical care etc. The Royal College of Anaesthetists are currently in the process of developing the QI dashboard and hope to make further additions in the next few months. All outputs will be aggregated with small numbers suppressed and will follow the ONS/HES guide on reporting.	The NELA audit is highly relevant to current clinical practice and publications will allow widespread disseminate of the findings amongst health professionals. Linkage to the HES/ONS data allows the Audit to report more extensively on patterns of care beyond the initial hospital admission and longer-term outcomes, such as 90-day mortality. The Audit is able to examine issues such as readmission rates and the most common reasons for these post-discharge complications, e.g.: respiratory complications and anastomotic leaks. The audit will produce useful indicators that describe the standard of care in a variety of clinical areas. The indicators will identify NHS providers that are performing well and those requiring improvement to the quality of care received by patients. Ongoing improvement in the processes of care and clinical outcomes should lead to a reduction in the postoperative mortality rates and thus an overall improvement in patient outcomes. Outcomes will be measured by re-auditing individual sites and therefore regular data linkage would be required. It is hoped that this improvement in care would be identified by the end of the currently proposed commissioned audit period (Dec 2018). The intended audience for the audit annual reports are clinicians, healthcare professionals, Medical Directors, Chief Executives, audit managers, commissioners, NHS England, public and patients. Trusts will use the process indicators and outcomes reported in the annual reports to assess their care against national standards and benchmark against other NHS trusts. This will enable providers to identify areas requiring improvement and take action which in turn will provide a benefit to patient care. Reporting will identify whether NHS trusts are meeting national guidance such as NICE recommendations and will identify variations in the provision of care. The Audit outcomes such as postoperative mortality are risk-adjusted and any potential outlying trusts are identified as part of the Audit outlier policy. Any Trust showing as an alarm will be notified which will allow for investigation into the cause; this can be attributable to either data quality issues or clinical practice. This notification will enable the trust to address the cause and either review the data submitted to the Audit or their clinical practice. Any resultant improvements in clinical practice will directly impact on improvements in patient care. The trust profiles are publically available, providing transparency and enabling patient choice. Publishing in peer-reviewed journals will allow greater discussion of the strengths and weaknesses of the results, and will provide the benefit of peer-review of the work from third parties. It is anticipated that the reports produced as a result of the audit will contribute to clinical guidance and national policy.
ROYAL COLLEGE OF ANAESTHETISTS	ROYAL COLLEGE OF ANAESTHETISTS	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Emergency abdominal surgery (or emergency laparotomy) is associated with significant morbidity and mortality worldwide. The aim of The National Emergency Laparotomy Audit (NELA) is to enable the improvement of the quality of care of patients undergoing emergency laparotomy by providing high quality comparative information of the clinical practice and outcomes of all NHS providers of emergency laparotomy in England and Wales. NELA is a national clinical audit commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical and Patient Outcomes Programme (NCAPOP). HQIP have commissioned the Royal College of Anaesthetists (RCoA) to deliver the audit. RCoA are working in partnership with the Clinical Effectiveness Unit of the Royal College of Surgeons (RCS) and therefore make up the NELA project team. HQIP act as data controllers for the national clinical audit but do not have access to any data collected or analysed by the RCoA and RCS who are the data processors. The analysis by the NELA project team will only involve pseudonymised (with exception to full Date of Death and Cause of Death) datasets that combine information submitted by NHS hospitals and data supplied by NHS Digital / ONS. Members of the project team work at either the RCoA or Clinical Effectiveness Unit at the RCS, however analysis of the NHS Digital, ONS, and an extract of pseudonymised NELA data (including full Date of Death obtained via hospitals) will only be processed and stored at RCS. The RCoA and RCS wish to link the patient records submitted to NELA with the Hospital Episode Statistics (HES) records for those patients. The NELA records relate only to an individual admission, and by linking to inpatient HES data, the Audit will be able to provide more precise and relevant information to NHS hospitals by allowing the RCoA and RCS to describe longer term outcomes (e.g., readmission rates) and to improve RCoA’s and RCS’s risk-adjustment models by using the extensive information on comorbid conditions held within HES. (eg to calculate the Charlson Comorbidity score) The RCoA and RCS also wish to link the patient records submitted to NELA with the Office for National Statistics (ONS) Death Register on a quarterly basis to enable the Audit to monitor changes in postoperative outcomes (both short and longer-term mortality) for those patients. Access to this linked information will support this national clinical audit to improve the quality of care within NHS hospitals for a high-risk patient group. The objectives of the Audit are: 1. To enable secondary care providers to improve the delivery of care to patients undergoing emergency laparotomy using information produced by the audit; 2. To provide comparative information on the organisation of care by providers of Emergency Laparotomy. 3. To provide comparative information on patient outcomes following surgery for Emergency Laparotomy. 4. To facilitate the development of effective change (quality improvement) initiatives and thereby spread examples of best practice and help local providers make the best possible use of audit results In summary, the purpose of this request is to support national clinical audit, quality improvement within hospital, and research on methods to monitor surgical outcomes. The NELA team is planning to request patient level clinical data from Intensive Care National Audit and Research Centre (ICNARC).	The RCoA are the principal data processors for NELA and manage the extraction of the records from the NELA IT system. RCoA will send the file of patient identifiers and the NELA ID to NHS Digital for linkage to HES and ONS fields. Pseudonymised files (of which includes full date of death and Cause of Death (text)) from NHS Digital will contain the HES and ONS fields with the NELA ID variable added. The pseudonymised files of HES / ONS data (including Date of Death and Cause of Death) will be received by the RCS and held on their secure data server. In all cases, the data received from NHS Digital will not be linked back to the identifiable NELA database held at RCoA. An extract of pseudonymised data of which includes date of death is taken from the NELA database and sent to RCS. This data will be linked to the HES-ONS data via the NELA ID. Date of Death from the NELA database is provided as this includes an important data quality step. There are potential missed linkages if RCS and RCoA do not have this information when processing the data at RCS. This also helps to validate the data entered into NELA. No data provided by NHS Digital is sent to RCoA to correct fields in the NELA database. A copy of the de-identified data fields along with the unique NELA ID will be analysed by project team members from both RCS and RCoA at the RCS Clinical Effectiveness Unit only. The only identifiable fields received by RCS and RCoA analysts are ONS Date of Death and Cause of death. The full Date of Death is required to be able to calculate survival at multiple time points (30 day, 90 day, etc.). There will be no capacity for RCS / RCoA analysts to use the ONS Date of Death to identify any individual patients. Analysts from RCS / RCoA who work on the de-identified data set do not have access to the identifiable data set held within the NELA IT system and managed by the RCoA, nor the list of patient identifiers sent to NHS digital for linkage purposes. All individuals with access to the de-identified data are substantively employed by RCoA or RCS. The list of patient identifiers sent to NHS digital is only accessible by a senior member of the RCoA. RCoA or RCS will not be linking HES/ONS data with any other dataset (apart from an extract of NELA). Linkage with any other datasets would be subject to a future application and would be supported by an appropriate legal basis. The majority of the analysis involving the de-identified linked patient dataset will be conducted by the RCS statisticians who form part of the NELA Project Team. The remainder would consist of statisticians from RCoA would be involved in some of the analysis of the patient-level dataset and will be located at the RCS Clinical Effectiveness Unit to undertake this work. In either case, all individuals with access to the data are substantively employed by either RCoA, or RCS and are required to sign a Confidentiality Agreement before access is granted.	The linked dataset will be a product of this process and will enhance the quality of the comparative data for the audit in subsequent years. The NELA is commissioned to produce a "State of the Nation" annual report each year. The first 2 reports utilising patient level information were published in June 2015 & June 2016 and are available to view on the NELA website. Subsequent reports are scheduled to be published yearly. In order to more widely disseminate the findings of the audit, additional scientific publications will be produced. These outputs will be in the form of peer-review articles and conference presentations. The results of the audit will also be disseminated at professional medical conferences and in peer-reviewed journals (e.g. BMJ, BJA, ASGBI journal, AAGBI journal) at the time of the launch of the report or shortly after. Publications related to the Audit methods (e.g., a risk-adjustment model) rather than information of clinical practice and outcomes will be published on an ad hoc basis. In response to participant feedback a Quality Improvement Report Dashboard has been created on the NELA Online Web Tool to assist sites with audit data collection and to promote local Quality Improvement work (Local hospital data can only be viewed by registered local hospital participants). The Quality Improvement Report Dashboard will only provide local units with aggregated information to compare their performance against a national average. The figures available to each unit will be based on their own local data (supplied by the units) and the Dashboard may therefore present small numbers on some occasions. Each individual user has their own login to the webtool which gives them access to only their own hospital local data. To access the webtool they require a username and password. The first phase of the Dashboard which has now been launched focuses on Case Ascertainment and Patient Demographics. The Patient Demographics section allows local participants to view some basic information on their hospital's population of patients undergoing emergency laparotomy, and how it compares to the audit-wide average. It focuses on characteristics such as patient age and operative urgency. The Case Ascertainment aims at increasing case completeness and submission by providing a monthly list of cases entered/completed/not completed. The next phase of the Dashboard will focus much more on Quality Improvement, feeding back key QI indicators and comparing hospital's local data with national audit-wide averages. Some of the measures reported back will include; Documentation of risk, Direct Admission to critical care etc. The Royal College of Anaesthetists are currently in the process of developing the QI dashboard and hope to make further additions in the next few months. All outputs will be aggregated with small numbers suppressed and will follow the ONS/HES guide on reporting.	The NELA audit is highly relevant to current clinical practice and publications will allow widespread disseminate of the findings amongst health professionals. Linkage to the HES/ONS data allows the Audit to report more extensively on patterns of care beyond the initial hospital admission and longer-term outcomes, such as 90-day mortality. The Audit is able to examine issues such as readmission rates and the most common reasons for these post-discharge complications, e.g.: respiratory complications and anastomotic leaks. The audit will produce useful indicators that describe the standard of care in a variety of clinical areas. The indicators will identify NHS providers that are performing well and those requiring improvement to the quality of care received by patients. Ongoing improvement in the processes of care and clinical outcomes should lead to a reduction in the postoperative mortality rates and thus an overall improvement in patient outcomes. Outcomes will be measured by re-auditing individual sites and therefore regular data linkage would be required. It is hoped that this improvement in care would be identified by the end of the currently proposed commissioned audit period (Dec 2018). The intended audience for the audit annual reports are clinicians, healthcare professionals, Medical Directors, Chief Executives, audit managers, commissioners, NHS England, public and patients. Trusts will use the process indicators and outcomes reported in the annual reports to assess their care against national standards and benchmark against other NHS trusts. This will enable providers to identify areas requiring improvement and take action which in turn will provide a benefit to patient care. Reporting will identify whether NHS trusts are meeting national guidance such as NICE recommendations and will identify variations in the provision of care. The Audit outcomes such as postoperative mortality are risk-adjusted and any potential outlying trusts are identified as part of the Audit outlier policy. Any Trust showing as an alarm will be notified which will allow for investigation into the cause; this can be attributable to either data quality issues or clinical practice. This notification will enable the trust to address the cause and either review the data submitted to the Audit or their clinical practice. Any resultant improvements in clinical practice will directly impact on improvements in patient care. The trust profiles are publically available, providing transparency and enabling patient choice. Publishing in peer-reviewed journals will allow greater discussion of the strengths and weaknesses of the results, and will provide the benefit of peer-review of the work from third parties. It is anticipated that the reports produced as a result of the audit will contribute to clinical guidance and national policy.
ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The majority of women giving birth and babies born in the UK receive safe and effective care. However, the stillbirth rate is higher in the UK than in many other European countries.[http://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(10)62310-0.pdf] There is also evidence of substantial variation in the maternity care received by women during pregnancy and delivery across hospitals, as well as the outcomes. These patterns of variation are also not the same for women from different socio-economic and ethnic backgrounds. [Patterns of Maternity Care in English NHS Hospitals 2013/14. Royal College of Obstetricians and Gynaecologists. London, 2016: https://www.rcog.org.uk/globalassets/documents/guidelines/research--audit/maternity-indicators-2013-14_report2.pdf] To address these issues, high quality information on the processes and outcomes of care is required so that clinicians, NHS managers and policy makers can examine the extent to which current practice meets the array of guidelines and standards, and to compare services and maternal and neonatal outcomes among maternity units. Pregnant women and their families also require this information to enable them to make a more informed choice between the services available to them. Maternity care is becoming increasingly high profile and is a subject of great public interest. The introduction of the Safer Maternity Care Action Plan in October 2016, which includes the National Maternity and Perinatal Audit (NMPA) , highlights that maternity care is a priority area for the Secretary of State for Health. The aim of this new National Clinical Audit and Patient Outcomes Programme (NCAPOP) Audit is to deliver a clinically meaningful and methodologically robust audit of all NHS maternity services in England, Scotland and Wales, to inform decision making by CCGs, policy makers and clinicians, and support maternity services to improve the quality of care and outcomes for mothers and newborns. The NMPA is commissioned by the Health Quality Improvement Partnership (HQIP) on behalf of the English and Welsh Governments and the Health Department of the Scottish Government. It is being carried out by the Royal College of Obstetricians and Gynaecologists (RCOG), in partnership with the Royal College of Midwives (RCM), Royal College of Paediatrics and Child Health (RCPCH) and the London School of Hygiene and Tropical Medicine (LSHTM), all of which are registered charities. Under this agreement RCOG and LSHTM are data processors and will be the only parties accessing the record level data. RCPCH and RCM will provide expert clinical advice on high-level decisions regarding the Audit, help facilitate engagement from clinicians and organise quality improvement programmes and regional meetings where the Audit’s findings and recommendations will be disseminated. One of the key aims of the Audit is to create a nationwide database containing all births to enable the development of robust and clinically meaningful quality indicators for maternity care. The Audit will develop a set of performance indicators to allow maternity units to benchmark themselves against their peers. The indicators will facilitate the comparison of antenatal, intrapartum and postnatal care patterns and identify determinants of variation both regionally and nationally. The commissioned audit programme consists of three phases of work: - An ‘organisational survey’ to collect provider-level information on service delivery and the organisation of maternity care, which will contribute to a better understanding of the care provided to pregnant women; - A continuous clinical audit that produces information for maternity units to monitor patterns of care and maternal and perinatal outcomes; - A series of in-depth topic-specific, time-limited audits (‘sprint audits’), predominantly focusing on specific types of maternal and neonatal outcomes. The continuous clinical audit will use the following sources of patient-level maternity data: • Data extracted from NHS hospitals’ electronic maternity record systems/maternity information systems (MISs) in England and Wales, for which Section 251 approval has been gained. These databases include information along the complete care pathway, from antenatal booking through to postnatal care. The applicants are currently requesting data extracts from individual providers in England and Wales, which will be sent to the applicant directly via secure file transfer. This protocol will eventually be replaced by the use of national maternity datasets. For England this will be the new Maternity Services Data Set (MSDS), once the submission rate, data quality and completeness are sufficiently high, and data is available from NHS Digital. HQIP has specified within the NMPA contract that the Audit should not become dependent on the flow of processed data from the MSDS until this flow is established and access to it does not introduce additional risk or delay to the analysis and reporting of the Audit. Similarly, in Wales a new Maternity Indicators Data Set is being implemented, with regular submissions now achieved by four of the Welsh Health Boards. • The Scottish Birth Record (SBR), which contains data from providers’ MISs, and already has a high data quality and completeness, covering over 98% of Scottish births. The Audit will use this national data source rather than requesting separate extracts from each provider. • The Audit will also use data from routine hospital episode datasets such as Hospital Episode Statistics (HES) in England (pending DARS approval), Patient Episode Data for Wales (PEDW) in Wales and the Scottish Morbidity Record 02 (SMR-02) in Scotland (pending approval from the Information Services Division (ISD), Scotland), which contain administrative information about each hospital admission, including deliveries. This data is necessary to the Audit for several reasons. Firstly, knowledge of hospital admissions and diagnoses during and after delivery will allow the understanding of maternal and neonatal outcomes, and provides a greater level of detail on treatments that took place during delivery. Secondly, knowledge of diagnoses before delivery will shed light on case-mix, which is essential in performing risk-adjustment of the Audit results (which enables a fair comparison between providers). Finally, the completeness of routine hospital episode datasets is very high, and thus it can be used to validate data from other sources such as the data extracts from providers’ MISs. The datasets will be linked at a patient level to produce: • MIS-HES linked database for England; • MIS-PEDW linked database for Wales; • SMR-02/SBR-NRS linked database for Scotland. A dataset linked at a patient level has several advantages for the Audit. It will: 1) minimise – if not eliminate – the burden on clinical staff of data collection for the sole purpose of the Audit; 2) enable information to be provided on longitudinal patterns of care, for example, hospital readmission following delivery, (3) enable validation of data from each source, and (4) enable information to be collected on the clinical history of the women before pregnancy, and their health service use during pregnancy which is important for case-mix adjustment. A similar methodology was found to be effective in a pilot study conducted by the RCOG in 2013/14, which involved 18 NHS hospitals across the UK supplying MIS data to create a database consisting of 120,000 delivery records from 2012/13, which was then linked to the HES database. The study positively demonstrated the feasibility of this approach and showed a very high level of completeness of essential data items (>98%) and data linkage. The Audit will provide all NHS providers, commissioners and clinical networks with individualised and timely feedback on the quality of care provided and maternal and neonatal outcomes. Patients and the wider public will have access to lay summaries of all Audit outputs.	For the continuous audit in England, until such a point that the new Maternity Services Data Set is mature, maternity units will supply the NMPA team with an annual extract of patient-level data (including babies’ and mothers’ dates of birth, babies’ and mothers’ NHS numbers, mothers’ postcodes and babies’ genders as well as clinical information about the care received by mothers and babies) relating to the deliveries that occurred at their unit in the previous financial year period (12 months) from their MIS. However, for the first extract, recently requested from all providers, data is required that relates to deliveries between 1st April 2014 and 31st March 2016. From then on, at the end of each year, the Audit team will request data for the previous financial year (e.g. in late 2017 the Audit team will request data on deliveries between 1st April 2016 and 31st March 2017), so that the study cohort is continually updated on annual basis). NHS Trusts will provide data from their MIS by transferring it to the NMPA’s secure server within the N3 network using a Secure File Transfer Protocol. All data processing will take place on this server. The secure server is leased from RedCentric by the RCOG, and is based at the RedCentric site in Reading, with backups located at the RedCentric Harrogate site. Data will be pseudonymised by the Audit’s two data managers, who will separate the patient identifiers contained within the data extracts from maternity record and treatment MIS data. Both data managers are based at the RCOG and hold substantive contracts of employment there. No other individuals will have access to patient identifiers. The records belonging to the same individual will only be accessed by the project team with a NMPA-derived anonymised label (which will be the study ID). All individuals with access to the record level data are substantive employees of either LSHTM or RCOG. The Audit’s data managers will securely transfer the patient identifiers to NHS Digital’s Data Linkage and Extract Service, where the following are being requested: 1) Linkage of the study cohort's patient identifiers plus study ID to HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data. This cohort will include mothers who gave birth and babies born from 1st April 2014 to 31st March 2016 (two financial years at the start) in the first instance, with the intention of updating the cohort on an annual basis (providing the latest information on those patients already linked, and the latest information with back data on new patients). The identifiers will be stripped out of the returned file and the study ID appended. 2) An unlinked extract of HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data, from 2000/01 to the latest available. This will be filtered to all mothers who have given birth, and all babies born, with the output being anonymised in context. The data will form the NMPA ID-HESdatabase, which the NMPA’s data managers will link to the pseudonymised MIS data. Patient identifiers will be stored separately and only the data managers will be able to access these. English patient data will not be transferred outside of England or linked to the Scottish or Welsh data at patient level. The linked datasets described above will provide a framework for continuous monitoring of processes and outcomes of maternity services using a comprehensive set of performance indicators linked to national standards, for example: • Antenatal care booking by 13 weeks of gestation (NICE NG4; QS22); • Proportion of elective caesareans performed before 39 completed weeks of gestation without a clinical indication (NICE CG132); • Proportion of infants with Apgar score less than 7 at 5 minutes among term, normally formed, singleton infants (Standards for Maternity Care). These indicators will be used to compare maternity services at national, regional, commissioner and unit level. The development of these indicators will be guided by criteria related to validity, statistical power, fairness and the appropriateness of the technical coding. The historical (from 2014) and future data (up to April 2018) on the individuals in the HES-MIS study cohort described above is required for two main reasons: 1) it enables information to be provided on longitudinal patterns of care, for example, maternal or neonatal hospital readmission following delivery, and (2) it enables information to be collected on the clinical history of the women before pregnancy and their health service use during pregnancy which is important for case-mix adjustment. The (unlinked) extract of HES data on women who gave birth in England since 1 April 2000, and babies born in this time period, allows the NMPA team to detect trends in patterns of care and in data quality over time. This is important in order to understand the Audit’s results and to put them into context. Risk adjustment approaches will be developed for the purpose of making the comparisons at the above-described levels ‘fair’ (as much as possible eliminating the impact of difference in case-mix). It is envisaged that the risk adjustment approach will need to vary according to the level of comparison, the type of indicators used (e.g. related to process and outcome) and the specific audit population. The statistical techniques will depend on the type of indicator involved. Logistic regression models will be used for indicators based on categorical variables, linear regression for indicators based on continuous variables and Cox or Poisson regression for indicators based on time-to-event data. Where necessary, multiple-imputation techniques will be used to handle records of patients with missing data, as well as multi-level modelling to take into account that results may be ‘clustered’ within maternity units or within other relevant units of analysis. All outputs will be aggregated and anonymised in line with the HES analysis guide. Any references to Mortality data regards Scottish Mortality data only.	From the start of the Audit, a reporting framework will be developed that produces frequent, individualised and timely output using online feedback to NHS providers, commissioners and networks. Summaries of all outputs for patients and the wider public will be produced. There will be five different approaches to report the results: 1. Annual reports (two versions – one version for providers and a lay version for patients and the public) will be used to report on adherence to national guidelines on essential aspects of maternity care, maternal and perinatal outcomes and trends over time. Variation in outcomes will be reported, carefully adjusted for differences in case-mix. The first annual reports will be published on 9th November 2017, with subsequent reports published in November 2018 and 2019. 2. Annual stakeholder meetings will be arranged to disseminate Audit findings and promote quality improvement. The first of these will be held on 9th November 2017, with subsequent events held in November 2018-January 2019 and November 2019-January 2020. 3. Online reports will be set up that allow individual providers, commissioners and relevant clinical networks to benchmark their process and outcomes indicators against care provided nationally and regionally. These reports will be designed to facilitate the use of national data for local audit activities. Moreover, the Audit will support English maternity units to contribute to the Quality Accounts. The online reporting system will be ready for use by providers by December 2017. This will be developed into a system of continuous monitoring, by December 2018, with the potential to update feedback about processes and outcomes of maternity services as soon as data become available. 4. From the Audit’s second year, it is envisaged that annually at least two reports of periodic time-limited, topic-specific audits will be produced to allow more detailed analysis and reporting than in the annual reports. These reports will be published by December 2018 and December 2019. 5. The Audit team will also produce peer-reviewed publications, especially related to the additional analyses aiming to identify determinants of variation in maternity services and methodological development work (e.g. risk adjustment, handling missing data, continuous monitoring, combining multiple linked indicators to assess maternity units’ performance, design of outputs that are most effective in local quality improvement). These publications will be submitted to clinical journals (e.g. British Journal of Obstetrics and Gynaecology or British Medical Journal) or methodological journals (such as the Journal of Clinical Epidemiology or the BMC Health Services Research). The submissions to journals will begin from summer 2017.	The Audit team will implement an active engagement strategy, communicating in a way that is accessible to all stakeholders. The Audit team are committed not just to the reporting of the results of the Audit in Annual Reports but to ensuring that the results lever local change and quality improvement. The Audit will provide robust and rigorous evidence to CCGs, to inform decisions on prioritising services for commissioning, and advise on the most effective ways to improve access to antenatal care. Results from the Audit will relate patterns of care to maternal and neonatal outcomes, guiding policies on, for example, the situations in which induction of labour, instrumental delivery and caesarean section lead to better or worse clinical outcomes. This will have a direct impact on clinical practice. The evidence-based clinical indicators derived in the Audit can be used by maternity units to assess their performance and compare it with others. Information will be made publically available, including key results at both individual maternity unit level and at regional levels reflecting the various commissioning structures in England. It will be ensured that appropriate regional comparisons can be made to allow an assessment of whether local maternity units and NHS commissioners are meeting relevant standards of care, including accepted national standards issued by NICE, RCOG, RCM, the British Association of Perinatal Medicine and the Obstetric Anaesthetists’ Association. This will inform decisions made by local managers on policies and procedures within maternity units. The Audit’s Annual Reports will include recommendations to enable NHS Trusts to drive effective local quality improvement initiatives. Some of these recommendations may be guided by providers who have been demonstrated to have superior performance according to the results of the Audit. The recommendations will be aimed at the full spectrum of stakeholders (e.g. individual clinicians, maternity units, commissioners or higher levels, depending on the issues at stake). These recommendations will also feed into quality improvement programmes in maternity care organised by the RCOG, RCM and RCPCH. Each College runs regular regional meetings and the Audit results will feed into their processes with the aim of standardising the delivery of care and improving the culture of safety for service users. Giving birth is the most common reason for admission to hospital in the UK, with approximately 800,000 births per year throughout England, Scotland and Wales. Thus each benefit described above has the potential to positively affect the experience of maternity care for a very large number of women and their families
ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The majority of women giving birth and babies born in the UK receive safe and effective care. However, the stillbirth rate is higher in the UK than in many other European countries.[http://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(10)62310-0.pdf] There is also evidence of substantial variation in the maternity care received by women during pregnancy and delivery across hospitals, as well as the outcomes. These patterns of variation are also not the same for women from different socio-economic and ethnic backgrounds. [Patterns of Maternity Care in English NHS Hospitals 2013/14. Royal College of Obstetricians and Gynaecologists. London, 2016: https://www.rcog.org.uk/globalassets/documents/guidelines/research--audit/maternity-indicators-2013-14_report2.pdf] To address these issues, high quality information on the processes and outcomes of care is required so that clinicians, NHS managers and policy makers can examine the extent to which current practice meets the array of guidelines and standards, and to compare services and maternal and neonatal outcomes among maternity units. Pregnant women and their families also require this information to enable them to make a more informed choice between the services available to them. Maternity care is becoming increasingly high profile and is a subject of great public interest. The introduction of the Safer Maternity Care Action Plan in October 2016, which includes the National Maternity and Perinatal Audit (NMPA) , highlights that maternity care is a priority area for the Secretary of State for Health. The aim of this new National Clinical Audit and Patient Outcomes Programme (NCAPOP) Audit is to deliver a clinically meaningful and methodologically robust audit of all NHS maternity services in England, Scotland and Wales, to inform decision making by CCGs, policy makers and clinicians, and support maternity services to improve the quality of care and outcomes for mothers and newborns. The NMPA is commissioned by the Health Quality Improvement Partnership (HQIP) on behalf of the English and Welsh Governments and the Health Department of the Scottish Government. It is being carried out by the Royal College of Obstetricians and Gynaecologists (RCOG), in partnership with the Royal College of Midwives (RCM), Royal College of Paediatrics and Child Health (RCPCH) and the London School of Hygiene and Tropical Medicine (LSHTM), all of which are registered charities. Under this agreement RCOG and LSHTM are data processors and will be the only parties accessing the record level data. RCPCH and RCM will provide expert clinical advice on high-level decisions regarding the Audit, help facilitate engagement from clinicians and organise quality improvement programmes and regional meetings where the Audit’s findings and recommendations will be disseminated. One of the key aims of the Audit is to create a nationwide database containing all births to enable the development of robust and clinically meaningful quality indicators for maternity care. The Audit will develop a set of performance indicators to allow maternity units to benchmark themselves against their peers. The indicators will facilitate the comparison of antenatal, intrapartum and postnatal care patterns and identify determinants of variation both regionally and nationally. The commissioned audit programme consists of three phases of work: - An ‘organisational survey’ to collect provider-level information on service delivery and the organisation of maternity care, which will contribute to a better understanding of the care provided to pregnant women; - A continuous clinical audit that produces information for maternity units to monitor patterns of care and maternal and perinatal outcomes; - A series of in-depth topic-specific, time-limited audits (‘sprint audits’), predominantly focusing on specific types of maternal and neonatal outcomes. The continuous clinical audit will use the following sources of patient-level maternity data: • Data extracted from NHS hospitals’ electronic maternity record systems/maternity information systems (MISs) in England and Wales, for which Section 251 approval has been gained. These databases include information along the complete care pathway, from antenatal booking through to postnatal care. The applicants are currently requesting data extracts from individual providers in England and Wales, which will be sent to the applicant directly via secure file transfer. This protocol will eventually be replaced by the use of national maternity datasets. For England this will be the new Maternity Services Data Set (MSDS), once the submission rate, data quality and completeness are sufficiently high, and data is available from NHS Digital. HQIP has specified within the NMPA contract that the Audit should not become dependent on the flow of processed data from the MSDS until this flow is established and access to it does not introduce additional risk or delay to the analysis and reporting of the Audit. Similarly, in Wales a new Maternity Indicators Data Set is being implemented, with regular submissions now achieved by four of the Welsh Health Boards. • The Scottish Birth Record (SBR), which contains data from providers’ MISs, and already has a high data quality and completeness, covering over 98% of Scottish births. The Audit will use this national data source rather than requesting separate extracts from each provider. • The Audit will also use data from routine hospital episode datasets such as Hospital Episode Statistics (HES) in England (pending DARS approval), Patient Episode Data for Wales (PEDW) in Wales and the Scottish Morbidity Record 02 (SMR-02) in Scotland (pending approval from the Information Services Division (ISD), Scotland), which contain administrative information about each hospital admission, including deliveries. This data is necessary to the Audit for several reasons. Firstly, knowledge of hospital admissions and diagnoses during and after delivery will allow the understanding of maternal and neonatal outcomes, and provides a greater level of detail on treatments that took place during delivery. Secondly, knowledge of diagnoses before delivery will shed light on case-mix, which is essential in performing risk-adjustment of the Audit results (which enables a fair comparison between providers). Finally, the completeness of routine hospital episode datasets is very high, and thus it can be used to validate data from other sources such as the data extracts from providers’ MISs. The datasets will be linked at a patient level to produce: • MIS-HES linked database for England; • MIS-PEDW linked database for Wales; • SMR-02/SBR-NRS linked database for Scotland. A dataset linked at a patient level has several advantages for the Audit. It will: 1) minimise – if not eliminate – the burden on clinical staff of data collection for the sole purpose of the Audit; 2) enable information to be provided on longitudinal patterns of care, for example, hospital readmission following delivery, (3) enable validation of data from each source, and (4) enable information to be collected on the clinical history of the women before pregnancy, and their health service use during pregnancy which is important for case-mix adjustment. A similar methodology was found to be effective in a pilot study conducted by the RCOG in 2013/14, which involved 18 NHS hospitals across the UK supplying MIS data to create a database consisting of 120,000 delivery records from 2012/13, which was then linked to the HES database. The study positively demonstrated the feasibility of this approach and showed a very high level of completeness of essential data items (>98%) and data linkage. The Audit will provide all NHS providers, commissioners and clinical networks with individualised and timely feedback on the quality of care provided and maternal and neonatal outcomes. Patients and the wider public will have access to lay summaries of all Audit outputs.	For the continuous audit in England, until such a point that the new Maternity Services Data Set is mature, maternity units will supply the NMPA team with an annual extract of patient-level data (including babies’ and mothers’ dates of birth, babies’ and mothers’ NHS numbers, mothers’ postcodes and babies’ genders as well as clinical information about the care received by mothers and babies) relating to the deliveries that occurred at their unit in the previous financial year period (12 months) from their MIS. However, for the first extract, recently requested from all providers, data is required that relates to deliveries between 1st April 2014 and 31st March 2016. From then on, at the end of each year, the Audit team will request data for the previous financial year (e.g. in late 2017 the Audit team will request data on deliveries between 1st April 2016 and 31st March 2017), so that the study cohort is continually updated on annual basis). NHS Trusts will provide data from their MIS by transferring it to the NMPA’s secure server within the N3 network using a Secure File Transfer Protocol. All data processing will take place on this server. The secure server is leased from RedCentric by the RCOG, and is based at the RedCentric site in Reading, with backups located at the RedCentric Harrogate site. Data will be pseudonymised by the Audit’s two data managers, who will separate the patient identifiers contained within the data extracts from maternity record and treatment MIS data. Both data managers are based at the RCOG and hold substantive contracts of employment there. No other individuals will have access to patient identifiers. The records belonging to the same individual will only be accessed by the project team with a NMPA-derived anonymised label (which will be the study ID). All individuals with access to the record level data are substantive employees of either LSHTM or RCOG. The Audit’s data managers will securely transfer the patient identifiers to NHS Digital’s Data Linkage and Extract Service, where the following are being requested: 1) Linkage of the study cohort's patient identifiers plus study ID to HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data. This cohort will include mothers who gave birth and babies born from 1st April 2014 to 31st March 2016 (two financial years at the start) in the first instance, with the intention of updating the cohort on an annual basis (providing the latest information on those patients already linked, and the latest information with back data on new patients). The identifiers will be stripped out of the returned file and the study ID appended. 2) An unlinked extract of HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data, from 2000/01 to the latest available. This will be filtered to all mothers who have given birth, and all babies born, with the output being anonymised in context. The data will form the NMPA ID-HESdatabase, which the NMPA’s data managers will link to the pseudonymised MIS data. Patient identifiers will be stored separately and only the data managers will be able to access these. English patient data will not be transferred outside of England or linked to the Scottish or Welsh data at patient level. The linked datasets described above will provide a framework for continuous monitoring of processes and outcomes of maternity services using a comprehensive set of performance indicators linked to national standards, for example: • Antenatal care booking by 13 weeks of gestation (NICE NG4; QS22); • Proportion of elective caesareans performed before 39 completed weeks of gestation without a clinical indication (NICE CG132); • Proportion of infants with Apgar score less than 7 at 5 minutes among term, normally formed, singleton infants (Standards for Maternity Care). These indicators will be used to compare maternity services at national, regional, commissioner and unit level. The development of these indicators will be guided by criteria related to validity, statistical power, fairness and the appropriateness of the technical coding. The historical (from 2014) and future data (up to April 2018) on the individuals in the HES-MIS study cohort described above is required for two main reasons: 1) it enables information to be provided on longitudinal patterns of care, for example, maternal or neonatal hospital readmission following delivery, and (2) it enables information to be collected on the clinical history of the women before pregnancy and their health service use during pregnancy which is important for case-mix adjustment. The (unlinked) extract of HES data on women who gave birth in England since 1 April 2000, and babies born in this time period, allows the NMPA team to detect trends in patterns of care and in data quality over time. This is important in order to understand the Audit’s results and to put them into context. Risk adjustment approaches will be developed for the purpose of making the comparisons at the above-described levels ‘fair’ (as much as possible eliminating the impact of difference in case-mix). It is envisaged that the risk adjustment approach will need to vary according to the level of comparison, the type of indicators used (e.g. related to process and outcome) and the specific audit population. The statistical techniques will depend on the type of indicator involved. Logistic regression models will be used for indicators based on categorical variables, linear regression for indicators based on continuous variables and Cox or Poisson regression for indicators based on time-to-event data. Where necessary, multiple-imputation techniques will be used to handle records of patients with missing data, as well as multi-level modelling to take into account that results may be ‘clustered’ within maternity units or within other relevant units of analysis. All outputs will be aggregated and anonymised in line with the HES analysis guide. Any references to Mortality data regards Scottish Mortality data only.	From the start of the Audit, a reporting framework will be developed that produces frequent, individualised and timely output using online feedback to NHS providers, commissioners and networks. Summaries of all outputs for patients and the wider public will be produced. There will be five different approaches to report the results: 1. Annual reports (two versions – one version for providers and a lay version for patients and the public) will be used to report on adherence to national guidelines on essential aspects of maternity care, maternal and perinatal outcomes and trends over time. Variation in outcomes will be reported, carefully adjusted for differences in case-mix. The first annual reports will be published on 9th November 2017, with subsequent reports published in November 2018 and 2019. 2. Annual stakeholder meetings will be arranged to disseminate Audit findings and promote quality improvement. The first of these will be held on 9th November 2017, with subsequent events held in November 2018-January 2019 and November 2019-January 2020. 3. Online reports will be set up that allow individual providers, commissioners and relevant clinical networks to benchmark their process and outcomes indicators against care provided nationally and regionally. These reports will be designed to facilitate the use of national data for local audit activities. Moreover, the Audit will support English maternity units to contribute to the Quality Accounts. The online reporting system will be ready for use by providers by December 2017. This will be developed into a system of continuous monitoring, by December 2018, with the potential to update feedback about processes and outcomes of maternity services as soon as data become available. 4. From the Audit’s second year, it is envisaged that annually at least two reports of periodic time-limited, topic-specific audits will be produced to allow more detailed analysis and reporting than in the annual reports. These reports will be published by December 2018 and December 2019. 5. The Audit team will also produce peer-reviewed publications, especially related to the additional analyses aiming to identify determinants of variation in maternity services and methodological development work (e.g. risk adjustment, handling missing data, continuous monitoring, combining multiple linked indicators to assess maternity units’ performance, design of outputs that are most effective in local quality improvement). These publications will be submitted to clinical journals (e.g. British Journal of Obstetrics and Gynaecology or British Medical Journal) or methodological journals (such as the Journal of Clinical Epidemiology or the BMC Health Services Research). The submissions to journals will begin from summer 2017.	The Audit team will implement an active engagement strategy, communicating in a way that is accessible to all stakeholders. The Audit team are committed not just to the reporting of the results of the Audit in Annual Reports but to ensuring that the results lever local change and quality improvement. The Audit will provide robust and rigorous evidence to CCGs, to inform decisions on prioritising services for commissioning, and advise on the most effective ways to improve access to antenatal care. Results from the Audit will relate patterns of care to maternal and neonatal outcomes, guiding policies on, for example, the situations in which induction of labour, instrumental delivery and caesarean section lead to better or worse clinical outcomes. This will have a direct impact on clinical practice. The evidence-based clinical indicators derived in the Audit can be used by maternity units to assess their performance and compare it with others. Information will be made publically available, including key results at both individual maternity unit level and at regional levels reflecting the various commissioning structures in England. It will be ensured that appropriate regional comparisons can be made to allow an assessment of whether local maternity units and NHS commissioners are meeting relevant standards of care, including accepted national standards issued by NICE, RCOG, RCM, the British Association of Perinatal Medicine and the Obstetric Anaesthetists’ Association. This will inform decisions made by local managers on policies and procedures within maternity units. The Audit’s Annual Reports will include recommendations to enable NHS Trusts to drive effective local quality improvement initiatives. Some of these recommendations may be guided by providers who have been demonstrated to have superior performance according to the results of the Audit. The recommendations will be aimed at the full spectrum of stakeholders (e.g. individual clinicians, maternity units, commissioners or higher levels, depending on the issues at stake). These recommendations will also feed into quality improvement programmes in maternity care organised by the RCOG, RCM and RCPCH. Each College runs regular regional meetings and the Audit results will feed into their processes with the aim of standardising the delivery of care and improving the culture of safety for service users. Giving birth is the most common reason for admission to hospital in the UK, with approximately 800,000 births per year throughout England, Scotland and Wales. Thus each benefit described above has the potential to positively affect the experience of maternity care for a very large number of women and their families
ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The majority of women giving birth and babies born in the UK receive safe and effective care. However, the stillbirth rate is higher in the UK than in many other European countries.[http://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(10)62310-0.pdf] There is also evidence of substantial variation in the maternity care received by women during pregnancy and delivery across hospitals, as well as the outcomes. These patterns of variation are also not the same for women from different socio-economic and ethnic backgrounds. [Patterns of Maternity Care in English NHS Hospitals 2013/14. Royal College of Obstetricians and Gynaecologists. London, 2016: https://www.rcog.org.uk/globalassets/documents/guidelines/research--audit/maternity-indicators-2013-14_report2.pdf] To address these issues, high quality information on the processes and outcomes of care is required so that clinicians, NHS managers and policy makers can examine the extent to which current practice meets the array of guidelines and standards, and to compare services and maternal and neonatal outcomes among maternity units. Pregnant women and their families also require this information to enable them to make a more informed choice between the services available to them. Maternity care is becoming increasingly high profile and is a subject of great public interest. The introduction of the Safer Maternity Care Action Plan in October 2016, which includes the National Maternity and Perinatal Audit (NMPA) , highlights that maternity care is a priority area for the Secretary of State for Health. The aim of this new National Clinical Audit and Patient Outcomes Programme (NCAPOP) Audit is to deliver a clinically meaningful and methodologically robust audit of all NHS maternity services in England, Scotland and Wales, to inform decision making by CCGs, policy makers and clinicians, and support maternity services to improve the quality of care and outcomes for mothers and newborns. The NMPA is commissioned by the Health Quality Improvement Partnership (HQIP) on behalf of the English and Welsh Governments and the Health Department of the Scottish Government. It is being carried out by the Royal College of Obstetricians and Gynaecologists (RCOG), in partnership with the Royal College of Midwives (RCM), Royal College of Paediatrics and Child Health (RCPCH) and the London School of Hygiene and Tropical Medicine (LSHTM), all of which are registered charities. Under this agreement RCOG and LSHTM are data processors and will be the only parties accessing the record level data. RCPCH and RCM will provide expert clinical advice on high-level decisions regarding the Audit, help facilitate engagement from clinicians and organise quality improvement programmes and regional meetings where the Audit’s findings and recommendations will be disseminated. One of the key aims of the Audit is to create a nationwide database containing all births to enable the development of robust and clinically meaningful quality indicators for maternity care. The Audit will develop a set of performance indicators to allow maternity units to benchmark themselves against their peers. The indicators will facilitate the comparison of antenatal, intrapartum and postnatal care patterns and identify determinants of variation both regionally and nationally. The commissioned audit programme consists of three phases of work: - An ‘organisational survey’ to collect provider-level information on service delivery and the organisation of maternity care, which will contribute to a better understanding of the care provided to pregnant women; - A continuous clinical audit that produces information for maternity units to monitor patterns of care and maternal and perinatal outcomes; - A series of in-depth topic-specific, time-limited audits (‘sprint audits’), predominantly focusing on specific types of maternal and neonatal outcomes. The continuous clinical audit will use the following sources of patient-level maternity data: • Data extracted from NHS hospitals’ electronic maternity record systems/maternity information systems (MISs) in England and Wales, for which Section 251 approval has been gained. These databases include information along the complete care pathway, from antenatal booking through to postnatal care. The applicants are currently requesting data extracts from individual providers in England and Wales, which will be sent to the applicant directly via secure file transfer. This protocol will eventually be replaced by the use of national maternity datasets. For England this will be the new Maternity Services Data Set (MSDS), once the submission rate, data quality and completeness are sufficiently high, and data is available from NHS Digital. HQIP has specified within the NMPA contract that the Audit should not become dependent on the flow of processed data from the MSDS until this flow is established and access to it does not introduce additional risk or delay to the analysis and reporting of the Audit. Similarly, in Wales a new Maternity Indicators Data Set is being implemented, with regular submissions now achieved by four of the Welsh Health Boards. • The Scottish Birth Record (SBR), which contains data from providers’ MISs, and already has a high data quality and completeness, covering over 98% of Scottish births. The Audit will use this national data source rather than requesting separate extracts from each provider. • The Audit will also use data from routine hospital episode datasets such as Hospital Episode Statistics (HES) in England (pending DARS approval), Patient Episode Data for Wales (PEDW) in Wales and the Scottish Morbidity Record 02 (SMR-02) in Scotland (pending approval from the Information Services Division (ISD), Scotland), which contain administrative information about each hospital admission, including deliveries. This data is necessary to the Audit for several reasons. Firstly, knowledge of hospital admissions and diagnoses during and after delivery will allow the understanding of maternal and neonatal outcomes, and provides a greater level of detail on treatments that took place during delivery. Secondly, knowledge of diagnoses before delivery will shed light on case-mix, which is essential in performing risk-adjustment of the Audit results (which enables a fair comparison between providers). Finally, the completeness of routine hospital episode datasets is very high, and thus it can be used to validate data from other sources such as the data extracts from providers’ MISs. The datasets will be linked at a patient level to produce: • MIS-HES linked database for England; • MIS-PEDW linked database for Wales; • SMR-02/SBR-NRS linked database for Scotland. A dataset linked at a patient level has several advantages for the Audit. It will: 1) minimise – if not eliminate – the burden on clinical staff of data collection for the sole purpose of the Audit; 2) enable information to be provided on longitudinal patterns of care, for example, hospital readmission following delivery, (3) enable validation of data from each source, and (4) enable information to be collected on the clinical history of the women before pregnancy, and their health service use during pregnancy which is important for case-mix adjustment. A similar methodology was found to be effective in a pilot study conducted by the RCOG in 2013/14, which involved 18 NHS hospitals across the UK supplying MIS data to create a database consisting of 120,000 delivery records from 2012/13, which was then linked to the HES database. The study positively demonstrated the feasibility of this approach and showed a very high level of completeness of essential data items (>98%) and data linkage. The Audit will provide all NHS providers, commissioners and clinical networks with individualised and timely feedback on the quality of care provided and maternal and neonatal outcomes. Patients and the wider public will have access to lay summaries of all Audit outputs.	For the continuous audit in England, until such a point that the new Maternity Services Data Set is mature, maternity units will supply the NMPA team with an annual extract of patient-level data (including babies’ and mothers’ dates of birth, babies’ and mothers’ NHS numbers, mothers’ postcodes and babies’ genders as well as clinical information about the care received by mothers and babies) relating to the deliveries that occurred at their unit in the previous financial year period (12 months) from their MIS. However, for the first extract, recently requested from all providers, data is required that relates to deliveries between 1st April 2014 and 31st March 2016. From then on, at the end of each year, the Audit team will request data for the previous financial year (e.g. in late 2017 the Audit team will request data on deliveries between 1st April 2016 and 31st March 2017), so that the study cohort is continually updated on annual basis). NHS Trusts will provide data from their MIS by transferring it to the NMPA’s secure server within the N3 network using a Secure File Transfer Protocol. All data processing will take place on this server. The secure server is leased from RedCentric by the RCOG, and is based at the RedCentric site in Reading, with backups located at the RedCentric Harrogate site. Data will be pseudonymised by the Audit’s two data managers, who will separate the patient identifiers contained within the data extracts from maternity record and treatment MIS data. Both data managers are based at the RCOG and hold substantive contracts of employment there. No other individuals will have access to patient identifiers. The records belonging to the same individual will only be accessed by the project team with a NMPA-derived anonymised label (which will be the study ID). All individuals with access to the record level data are substantive employees of either LSHTM or RCOG. The Audit’s data managers will securely transfer the patient identifiers to NHS Digital’s Data Linkage and Extract Service, where the following are being requested: 1) Linkage of the study cohort's patient identifiers plus study ID to HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data. This cohort will include mothers who gave birth and babies born from 1st April 2014 to 31st March 2016 (two financial years at the start) in the first instance, with the intention of updating the cohort on an annual basis (providing the latest information on those patients already linked, and the latest information with back data on new patients). The identifiers will be stripped out of the returned file and the study ID appended. 2) An unlinked extract of HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data, from 2000/01 to the latest available. This will be filtered to all mothers who have given birth, and all babies born, with the output being anonymised in context. The data will form the NMPA ID-HESdatabase, which the NMPA’s data managers will link to the pseudonymised MIS data. Patient identifiers will be stored separately and only the data managers will be able to access these. English patient data will not be transferred outside of England or linked to the Scottish or Welsh data at patient level. The linked datasets described above will provide a framework for continuous monitoring of processes and outcomes of maternity services using a comprehensive set of performance indicators linked to national standards, for example: • Antenatal care booking by 13 weeks of gestation (NICE NG4; QS22); • Proportion of elective caesareans performed before 39 completed weeks of gestation without a clinical indication (NICE CG132); • Proportion of infants with Apgar score less than 7 at 5 minutes among term, normally formed, singleton infants (Standards for Maternity Care). These indicators will be used to compare maternity services at national, regional, commissioner and unit level. The development of these indicators will be guided by criteria related to validity, statistical power, fairness and the appropriateness of the technical coding. The historical (from 2014) and future data (up to April 2018) on the individuals in the HES-MIS study cohort described above is required for two main reasons: 1) it enables information to be provided on longitudinal patterns of care, for example, maternal or neonatal hospital readmission following delivery, and (2) it enables information to be collected on the clinical history of the women before pregnancy and their health service use during pregnancy which is important for case-mix adjustment. The (unlinked) extract of HES data on women who gave birth in England since 1 April 2000, and babies born in this time period, allows the NMPA team to detect trends in patterns of care and in data quality over time. This is important in order to understand the Audit’s results and to put them into context. Risk adjustment approaches will be developed for the purpose of making the comparisons at the above-described levels ‘fair’ (as much as possible eliminating the impact of difference in case-mix). It is envisaged that the risk adjustment approach will need to vary according to the level of comparison, the type of indicators used (e.g. related to process and outcome) and the specific audit population. The statistical techniques will depend on the type of indicator involved. Logistic regression models will be used for indicators based on categorical variables, linear regression for indicators based on continuous variables and Cox or Poisson regression for indicators based on time-to-event data. Where necessary, multiple-imputation techniques will be used to handle records of patients with missing data, as well as multi-level modelling to take into account that results may be ‘clustered’ within maternity units or within other relevant units of analysis. All outputs will be aggregated and anonymised in line with the HES analysis guide. Any references to Mortality data regards Scottish Mortality data only.	From the start of the Audit, a reporting framework will be developed that produces frequent, individualised and timely output using online feedback to NHS providers, commissioners and networks. Summaries of all outputs for patients and the wider public will be produced. There will be five different approaches to report the results: 1. Annual reports (two versions – one version for providers and a lay version for patients and the public) will be used to report on adherence to national guidelines on essential aspects of maternity care, maternal and perinatal outcomes and trends over time. Variation in outcomes will be reported, carefully adjusted for differences in case-mix. The first annual reports will be published on 9th November 2017, with subsequent reports published in November 2018 and 2019. 2. Annual stakeholder meetings will be arranged to disseminate Audit findings and promote quality improvement. The first of these will be held on 9th November 2017, with subsequent events held in November 2018-January 2019 and November 2019-January 2020. 3. Online reports will be set up that allow individual providers, commissioners and relevant clinical networks to benchmark their process and outcomes indicators against care provided nationally and regionally. These reports will be designed to facilitate the use of national data for local audit activities. Moreover, the Audit will support English maternity units to contribute to the Quality Accounts. The online reporting system will be ready for use by providers by December 2017. This will be developed into a system of continuous monitoring, by December 2018, with the potential to update feedback about processes and outcomes of maternity services as soon as data become available. 4. From the Audit’s second year, it is envisaged that annually at least two reports of periodic time-limited, topic-specific audits will be produced to allow more detailed analysis and reporting than in the annual reports. These reports will be published by December 2018 and December 2019. 5. The Audit team will also produce peer-reviewed publications, especially related to the additional analyses aiming to identify determinants of variation in maternity services and methodological development work (e.g. risk adjustment, handling missing data, continuous monitoring, combining multiple linked indicators to assess maternity units’ performance, design of outputs that are most effective in local quality improvement). These publications will be submitted to clinical journals (e.g. British Journal of Obstetrics and Gynaecology or British Medical Journal) or methodological journals (such as the Journal of Clinical Epidemiology or the BMC Health Services Research). The submissions to journals will begin from summer 2017.	The Audit team will implement an active engagement strategy, communicating in a way that is accessible to all stakeholders. The Audit team are committed not just to the reporting of the results of the Audit in Annual Reports but to ensuring that the results lever local change and quality improvement. The Audit will provide robust and rigorous evidence to CCGs, to inform decisions on prioritising services for commissioning, and advise on the most effective ways to improve access to antenatal care. Results from the Audit will relate patterns of care to maternal and neonatal outcomes, guiding policies on, for example, the situations in which induction of labour, instrumental delivery and caesarean section lead to better or worse clinical outcomes. This will have a direct impact on clinical practice. The evidence-based clinical indicators derived in the Audit can be used by maternity units to assess their performance and compare it with others. Information will be made publically available, including key results at both individual maternity unit level and at regional levels reflecting the various commissioning structures in England. It will be ensured that appropriate regional comparisons can be made to allow an assessment of whether local maternity units and NHS commissioners are meeting relevant standards of care, including accepted national standards issued by NICE, RCOG, RCM, the British Association of Perinatal Medicine and the Obstetric Anaesthetists’ Association. This will inform decisions made by local managers on policies and procedures within maternity units. The Audit’s Annual Reports will include recommendations to enable NHS Trusts to drive effective local quality improvement initiatives. Some of these recommendations may be guided by providers who have been demonstrated to have superior performance according to the results of the Audit. The recommendations will be aimed at the full spectrum of stakeholders (e.g. individual clinicians, maternity units, commissioners or higher levels, depending on the issues at stake). These recommendations will also feed into quality improvement programmes in maternity care organised by the RCOG, RCM and RCPCH. Each College runs regular regional meetings and the Audit results will feed into their processes with the aim of standardising the delivery of care and improving the culture of safety for service users. Giving birth is the most common reason for admission to hospital in the UK, with approximately 800,000 births per year throughout England, Scotland and Wales. Thus each benefit described above has the potential to positively affect the experience of maternity care for a very large number of women and their families
ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	ROYAL COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS (RCOG)	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The majority of women giving birth and babies born in the UK receive safe and effective care. However, the stillbirth rate is higher in the UK than in many other European countries.[http://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(10)62310-0.pdf] There is also evidence of substantial variation in the maternity care received by women during pregnancy and delivery across hospitals, as well as the outcomes. These patterns of variation are also not the same for women from different socio-economic and ethnic backgrounds. [Patterns of Maternity Care in English NHS Hospitals 2013/14. Royal College of Obstetricians and Gynaecologists. London, 2016: https://www.rcog.org.uk/globalassets/documents/guidelines/research--audit/maternity-indicators-2013-14_report2.pdf] To address these issues, high quality information on the processes and outcomes of care is required so that clinicians, NHS managers and policy makers can examine the extent to which current practice meets the array of guidelines and standards, and to compare services and maternal and neonatal outcomes among maternity units. Pregnant women and their families also require this information to enable them to make a more informed choice between the services available to them. Maternity care is becoming increasingly high profile and is a subject of great public interest. The introduction of the Safer Maternity Care Action Plan in October 2016, which includes the National Maternity and Perinatal Audit (NMPA) , highlights that maternity care is a priority area for the Secretary of State for Health. The aim of this new National Clinical Audit and Patient Outcomes Programme (NCAPOP) Audit is to deliver a clinically meaningful and methodologically robust audit of all NHS maternity services in England, Scotland and Wales, to inform decision making by CCGs, policy makers and clinicians, and support maternity services to improve the quality of care and outcomes for mothers and newborns. The NMPA is commissioned by the Health Quality Improvement Partnership (HQIP) on behalf of the English and Welsh Governments and the Health Department of the Scottish Government. It is being carried out by the Royal College of Obstetricians and Gynaecologists (RCOG), in partnership with the Royal College of Midwives (RCM), Royal College of Paediatrics and Child Health (RCPCH) and the London School of Hygiene and Tropical Medicine (LSHTM), all of which are registered charities. Under this agreement RCOG and LSHTM are data processors and will be the only parties accessing the record level data. RCPCH and RCM will provide expert clinical advice on high-level decisions regarding the Audit, help facilitate engagement from clinicians and organise quality improvement programmes and regional meetings where the Audit’s findings and recommendations will be disseminated. One of the key aims of the Audit is to create a nationwide database containing all births to enable the development of robust and clinically meaningful quality indicators for maternity care. The Audit will develop a set of performance indicators to allow maternity units to benchmark themselves against their peers. The indicators will facilitate the comparison of antenatal, intrapartum and postnatal care patterns and identify determinants of variation both regionally and nationally. The commissioned audit programme consists of three phases of work: - An ‘organisational survey’ to collect provider-level information on service delivery and the organisation of maternity care, which will contribute to a better understanding of the care provided to pregnant women; - A continuous clinical audit that produces information for maternity units to monitor patterns of care and maternal and perinatal outcomes; - A series of in-depth topic-specific, time-limited audits (‘sprint audits’), predominantly focusing on specific types of maternal and neonatal outcomes. The continuous clinical audit will use the following sources of patient-level maternity data: • Data extracted from NHS hospitals’ electronic maternity record systems/maternity information systems (MISs) in England and Wales, for which Section 251 approval has been gained. These databases include information along the complete care pathway, from antenatal booking through to postnatal care. The applicants are currently requesting data extracts from individual providers in England and Wales, which will be sent to the applicant directly via secure file transfer. This protocol will eventually be replaced by the use of national maternity datasets. For England this will be the new Maternity Services Data Set (MSDS), once the submission rate, data quality and completeness are sufficiently high, and data is available from NHS Digital. HQIP has specified within the NMPA contract that the Audit should not become dependent on the flow of processed data from the MSDS until this flow is established and access to it does not introduce additional risk or delay to the analysis and reporting of the Audit. Similarly, in Wales a new Maternity Indicators Data Set is being implemented, with regular submissions now achieved by four of the Welsh Health Boards. • The Scottish Birth Record (SBR), which contains data from providers’ MISs, and already has a high data quality and completeness, covering over 98% of Scottish births. The Audit will use this national data source rather than requesting separate extracts from each provider. • The Audit will also use data from routine hospital episode datasets such as Hospital Episode Statistics (HES) in England (pending DARS approval), Patient Episode Data for Wales (PEDW) in Wales and the Scottish Morbidity Record 02 (SMR-02) in Scotland (pending approval from the Information Services Division (ISD), Scotland), which contain administrative information about each hospital admission, including deliveries. This data is necessary to the Audit for several reasons. Firstly, knowledge of hospital admissions and diagnoses during and after delivery will allow the understanding of maternal and neonatal outcomes, and provides a greater level of detail on treatments that took place during delivery. Secondly, knowledge of diagnoses before delivery will shed light on case-mix, which is essential in performing risk-adjustment of the Audit results (which enables a fair comparison between providers). Finally, the completeness of routine hospital episode datasets is very high, and thus it can be used to validate data from other sources such as the data extracts from providers’ MISs. The datasets will be linked at a patient level to produce: • MIS-HES linked database for England; • MIS-PEDW linked database for Wales; • SMR-02/SBR-NRS linked database for Scotland. A dataset linked at a patient level has several advantages for the Audit. It will: 1) minimise – if not eliminate – the burden on clinical staff of data collection for the sole purpose of the Audit; 2) enable information to be provided on longitudinal patterns of care, for example, hospital readmission following delivery, (3) enable validation of data from each source, and (4) enable information to be collected on the clinical history of the women before pregnancy, and their health service use during pregnancy which is important for case-mix adjustment. A similar methodology was found to be effective in a pilot study conducted by the RCOG in 2013/14, which involved 18 NHS hospitals across the UK supplying MIS data to create a database consisting of 120,000 delivery records from 2012/13, which was then linked to the HES database. The study positively demonstrated the feasibility of this approach and showed a very high level of completeness of essential data items (>98%) and data linkage. The Audit will provide all NHS providers, commissioners and clinical networks with individualised and timely feedback on the quality of care provided and maternal and neonatal outcomes. Patients and the wider public will have access to lay summaries of all Audit outputs.	For the continuous audit in England, until such a point that the new Maternity Services Data Set is mature, maternity units will supply the NMPA team with an annual extract of patient-level data (including babies’ and mothers’ dates of birth, babies’ and mothers’ NHS numbers, mothers’ postcodes and babies’ genders as well as clinical information about the care received by mothers and babies) relating to the deliveries that occurred at their unit in the previous financial year period (12 months) from their MIS. However, for the first extract, recently requested from all providers, data is required that relates to deliveries between 1st April 2014 and 31st March 2016. From then on, at the end of each year, the Audit team will request data for the previous financial year (e.g. in late 2017 the Audit team will request data on deliveries between 1st April 2016 and 31st March 2017), so that the study cohort is continually updated on annual basis). NHS Trusts will provide data from their MIS by transferring it to the NMPA’s secure server within the N3 network using a Secure File Transfer Protocol. All data processing will take place on this server. The secure server is leased from RedCentric by the RCOG, and is based at the RedCentric site in Reading, with backups located at the RedCentric Harrogate site. Data will be pseudonymised by the Audit’s two data managers, who will separate the patient identifiers contained within the data extracts from maternity record and treatment MIS data. Both data managers are based at the RCOG and hold substantive contracts of employment there. No other individuals will have access to patient identifiers. The records belonging to the same individual will only be accessed by the project team with a NMPA-derived anonymised label (which will be the study ID). All individuals with access to the record level data are substantive employees of either LSHTM or RCOG. The Audit’s data managers will securely transfer the patient identifiers to NHS Digital’s Data Linkage and Extract Service, where the following are being requested: 1) Linkage of the study cohort's patient identifiers plus study ID to HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data. This cohort will include mothers who gave birth and babies born from 1st April 2014 to 31st March 2016 (two financial years at the start) in the first instance, with the intention of updating the cohort on an annual basis (providing the latest information on those patients already linked, and the latest information with back data on new patients). The identifiers will be stripped out of the returned file and the study ID appended. 2) An unlinked extract of HES Admitted Patient Care, Critical Care, Outpatient and Accident & Emergency data, from 2000/01 to the latest available. This will be filtered to all mothers who have given birth, and all babies born, with the output being anonymised in context. The data will form the NMPA ID-HESdatabase, which the NMPA’s data managers will link to the pseudonymised MIS data. Patient identifiers will be stored separately and only the data managers will be able to access these. English patient data will not be transferred outside of England or linked to the Scottish or Welsh data at patient level. The linked datasets described above will provide a framework for continuous monitoring of processes and outcomes of maternity services using a comprehensive set of performance indicators linked to national standards, for example: • Antenatal care booking by 13 weeks of gestation (NICE NG4; QS22); • Proportion of elective caesareans performed before 39 completed weeks of gestation without a clinical indication (NICE CG132); • Proportion of infants with Apgar score less than 7 at 5 minutes among term, normally formed, singleton infants (Standards for Maternity Care). These indicators will be used to compare maternity services at national, regional, commissioner and unit level. The development of these indicators will be guided by criteria related to validity, statistical power, fairness and the appropriateness of the technical coding. The historical (from 2014) and future data (up to April 2018) on the individuals in the HES-MIS study cohort described above is required for two main reasons: 1) it enables information to be provided on longitudinal patterns of care, for example, maternal or neonatal hospital readmission following delivery, and (2) it enables information to be collected on the clinical history of the women before pregnancy and their health service use during pregnancy which is important for case-mix adjustment. The (unlinked) extract of HES data on women who gave birth in England since 1 April 2000, and babies born in this time period, allows the NMPA team to detect trends in patterns of care and in data quality over time. This is important in order to understand the Audit’s results and to put them into context. Risk adjustment approaches will be developed for the purpose of making the comparisons at the above-described levels ‘fair’ (as much as possible eliminating the impact of difference in case-mix). It is envisaged that the risk adjustment approach will need to vary according to the level of comparison, the type of indicators used (e.g. related to process and outcome) and the specific audit population. The statistical techniques will depend on the type of indicator involved. Logistic regression models will be used for indicators based on categorical variables, linear regression for indicators based on continuous variables and Cox or Poisson regression for indicators based on time-to-event data. Where necessary, multiple-imputation techniques will be used to handle records of patients with missing data, as well as multi-level modelling to take into account that results may be ‘clustered’ within maternity units or within other relevant units of analysis. All outputs will be aggregated and anonymised in line with the HES analysis guide. Any references to Mortality data regards Scottish Mortality data only.	From the start of the Audit, a reporting framework will be developed that produces frequent, individualised and timely output using online feedback to NHS providers, commissioners and networks. Summaries of all outputs for patients and the wider public will be produced. There will be five different approaches to report the results: 1. Annual reports (two versions – one version for providers and a lay version for patients and the public) will be used to report on adherence to national guidelines on essential aspects of maternity care, maternal and perinatal outcomes and trends over time. Variation in outcomes will be reported, carefully adjusted for differences in case-mix. The first annual reports will be published on 9th November 2017, with subsequent reports published in November 2018 and 2019. 2. Annual stakeholder meetings will be arranged to disseminate Audit findings and promote quality improvement. The first of these will be held on 9th November 2017, with subsequent events held in November 2018-January 2019 and November 2019-January 2020. 3. Online reports will be set up that allow individual providers, commissioners and relevant clinical networks to benchmark their process and outcomes indicators against care provided nationally and regionally. These reports will be designed to facilitate the use of national data for local audit activities. Moreover, the Audit will support English maternity units to contribute to the Quality Accounts. The online reporting system will be ready for use by providers by December 2017. This will be developed into a system of continuous monitoring, by December 2018, with the potential to update feedback about processes and outcomes of maternity services as soon as data become available. 4. From the Audit’s second year, it is envisaged that annually at least two reports of periodic time-limited, topic-specific audits will be produced to allow more detailed analysis and reporting than in the annual reports. These reports will be published by December 2018 and December 2019. 5. The Audit team will also produce peer-reviewed publications, especially related to the additional analyses aiming to identify determinants of variation in maternity services and methodological development work (e.g. risk adjustment, handling missing data, continuous monitoring, combining multiple linked indicators to assess maternity units’ performance, design of outputs that are most effective in local quality improvement). These publications will be submitted to clinical journals (e.g. British Journal of Obstetrics and Gynaecology or British Medical Journal) or methodological journals (such as the Journal of Clinical Epidemiology or the BMC Health Services Research). The submissions to journals will begin from summer 2017.	The Audit team will implement an active engagement strategy, communicating in a way that is accessible to all stakeholders. The Audit team are committed not just to the reporting of the results of the Audit in Annual Reports but to ensuring that the results lever local change and quality improvement. The Audit will provide robust and rigorous evidence to CCGs, to inform decisions on prioritising services for commissioning, and advise on the most effective ways to improve access to antenatal care. Results from the Audit will relate patterns of care to maternal and neonatal outcomes, guiding policies on, for example, the situations in which induction of labour, instrumental delivery and caesarean section lead to better or worse clinical outcomes. This will have a direct impact on clinical practice. The evidence-based clinical indicators derived in the Audit can be used by maternity units to assess their performance and compare it with others. Information will be made publically available, including key results at both individual maternity unit level and at regional levels reflecting the various commissioning structures in England. It will be ensured that appropriate regional comparisons can be made to allow an assessment of whether local maternity units and NHS commissioners are meeting relevant standards of care, including accepted national standards issued by NICE, RCOG, RCM, the British Association of Perinatal Medicine and the Obstetric Anaesthetists’ Association. This will inform decisions made by local managers on policies and procedures within maternity units. The Audit’s Annual Reports will include recommendations to enable NHS Trusts to drive effective local quality improvement initiatives. Some of these recommendations may be guided by providers who have been demonstrated to have superior performance according to the results of the Audit. The recommendations will be aimed at the full spectrum of stakeholders (e.g. individual clinicians, maternity units, commissioners or higher levels, depending on the issues at stake). These recommendations will also feed into quality improvement programmes in maternity care organised by the RCOG, RCM and RCPCH. Each College runs regular regional meetings and the Audit results will feed into their processes with the aim of standardising the delivery of care and improving the culture of safety for service users. Giving birth is the most common reason for admission to hospital in the UK, with approximately 800,000 births per year throughout England, Scotland and Wales. Thus each benefit described above has the potential to positively affect the experience of maternity care for a very large number of women and their families
ROYAL COLLEGE OF PHYSICIANS OF LONDON	ROYAL COLLEGE OF PHYSICIANS OF LONDON	MRIS - Flagging Current Status Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	ONS: The Royal College of Physicians (RCP) is the data processor responsible for producing the CCG Outcomes Indicator Set (CCGOIS) measure of mortality at 30 days for stroke patients. These results are provided to NHS Digital to publish as part of the wider CCGOIS. The results are also provided at team level to provide necessary context on the performance of clinical teams treating stroke patients. As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. It is also important that Royal College of Physicians are able to provide the information back to the clinical teams who have treated the patients. HES: The HES dataset is used to determine the case ascertainment (case ascertainment is a measure of the number of cases reported in the audit, compared to the number of cases identified in HES) of participants of the Sentinel Stroke National Audit Programme (SSNAP), that is, the proportion of coded stroke patients which are recorded in the audit; and identify any readmissions and further strokes, in order to compare quality of care with outcomes for patients. As the outputs of analysis of SSNAP are reported and publically available, the proportion of patients entered into the audit for each hospital team, compared with the numbers in HES, is vital in determining how results are used (for instance, if there is low case ascertainment, the mortality outcomes would not be reported so that there is no potential misrepresentation).	RCP will send cohort information to NHS Digital for linkage, they send NHS Number, Full postcode, Name, and a unique SSNAP ID. As part of the section 251 support, there is a method by which the information is sent to NHS Digital for linkage without the RCP viewing any patient identifiable information. NHS Digital return; • Non sensitive pseudonymised HES data with SSNAP ID for patients in cohort • Non sensitive pseudonymised HES data for patients with a diagnosis of stroke • Identifiable ONS date and cause of death RCP combine HES and ONS data with SSNAP data and combine into separate databases; one with SSNAP and ONS data and the other with SSNAP and HES data. Identifiers are held separately to other data and the pseudonym SSNAP ID is used to identify individual patients. With the exception of date of death, analysts access no identifiers. Pseudonymised HES Data is then analysed to calculate case ascertainment information for the audit. HES data is also used to validate some of the information collected in the audit. No HES data is stored or processed by Netsolving, all processing is undertaken at the RCP. Identifiable ONS data is analysed to produce 30 day mortality at CCG level and stroke team level (team usually equates to a hospital). Cause of death is used to disaggregate stroke specific deaths and deaths from other causes. For statistical purposes such as monitoring trends identifiable ONS death data is also passed back (via the secure webtool hosted by Netsolving) to registered individuals at participating trusts whereby they can access date of death for patients they submit to the audit. All arrangements for 3rd party access will be controlled through sublicensing agreements and will be for the benefit of health and care; all arrangements will be approved by the HSCIC before data being sent. All individuals with access to the data are substantive employees of the Royal College of Physicians of London.	ONS and HES: Indicators will be produced showing the performance of organisations and at national level for the purpose of monitoring and quality improvement, in particular: • Mortality within 30 days of hospital admission for stroke CCG Outcomes Indictor Set (CCGOIS) at least annually (first publication on 17 December 2014, next publication anticipated to be published by the end of 2017, subject to timely receipt of ONS data from NHS Digital) • Mortality within 30 days of hospital admission for stroke Team-level mortality results (published in line with CCGOIS and used for contextualising the results). (Team usually equates to a hospital). • Audit case ascertainment information • For statistical purposes such as monitoring trends registered individuals at Trusts can access date of death for patients they submit to the audit derived from ONS mortality data. • As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates and causes of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. • Planned analyses following agreement with our steering group investigating stroke rates and associations with comorbidities recorded in HES to be published in peer-reviewed journals	Case ascertainment information will be used to target trusts who are not achieving good levels of audit case ascertainment, this leads to more complete data and more valid results in future audit. Complete audit information is essential for service improvement, and improvements to stroke patient care. HES data is utilised to compare the number of records submitted to the audit, with the number recorded in HES. SSNAP are currently finalising the methodology for determining the number of new stroke admissions per team, which will then be used to better triangulate which teams are submitting fewer patients to the audit than they are recording in HES. The previous years’ HES denominators enabled the audit to target teams with poorer case ascertainment and to chase those teams before submission deadlines in order to maintain the national case ascertainment above 90%. In the latest round of reporting (for Aug-Nov 2016), it was estimated that 90% of routinely admitting teams in England and Wales submitted over 90% of their stroke cases to the audit. The percentage case ascertainment at teams is used to penalise teams with low case ascertainment, and functions as a key driver for high participation levels. This ongoing process is a key driver to high case ascertainment in the audit – comparing teams to HES has yielded a consistent 90%+ case ascertainment nationally, which is key to ensuring representativeness and usefulness of the audit data. The benefit of this the receipt of HES data is therefore to enable a useful audit dataset, and the use of previous years’ HES has ensured the audit’s reputation as a high quality data source, and enabled audit data to be used to highlight areas of unwarranted variation across the country, to identify key areas for improvement, and for use in a number of parliamentary questions. Mortality within 30 days of hospital admission is part of Domain 1 of the NHS CCG Outcome indicator set “reducing premature mortality”. CCGs will access the published information and use it to improve services through identification of good and bad practice. This will be of benefit both in terms of better value for money and better patient outcomes. Mortality at CCG level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. The CCG OIS mortality measure was last published by SSNAP and by NHS Digital in early 2017. CCG outliers were informed in late 2016 and conversations with the CCG chairs, and medical directors ensued. One CCG was flagged as an outlier, despite none of its constituent hospitals being flagged. This raised a question to be investigated by the CCG as to whether they needed to assess their commissioning of services. The analyses will also help CCGs and STPs in the debate around where services should be reconfigured by enabling the use of appropriately adjusted mortality information. Outlier CCGs will again be contacted using an outlier processes to discuss where improvements in stroke care are needed in order to benefit both in terms of better value for money and better patient outcomes. The data will be published on the SSNAP website, as well as part of the CCG OIS, and is normally considered for other outputs such as PHE’s CVD profiles, Atlas of Variation etc. Reporting and publishing this information in the future is key to ensuring CCGs with high mortality rates are informed of this, and have the opportunity to improve. Similarly, trusts will use team level mortality within 30 days of hospital admission to identify trends and good practice, again leading to better patient outcomes. Mortality at team level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. In the past year, the mortality outlier for 2015-16 requested a stroke peer review visit to help identify key ways to improve their service. A quality improvement programme is now being implemented in this service, following from a detailed peer review visit. Outlier teams will again be contacted using an outlier processes to discuss where improvements in stroke care are needed with the Chief Executive, medical director and clinical lead for stroke. This information will be put into the public domain so patients and the public can see which hospitals have poor outcomes, for example through the MyNHS website. A stroke peer review visit will be offered to outlying teams to assist with identifying key areas for improvement and ways to achieve that improvement. Feeding back mortality information to teams, allows teams to investigate their patient outcomes and put in place ways to improve, for example by investigating patient deaths following the use of thrombolysis. Statistical analyses investigating longer-term mortality will have the following benefits: • assessing the real-world benefit of new interventions such as intra-arterial intervention (mechanical thrombectomy) and intermittent pneumatic compression stockings • tracking changes in mortality trends over time • monitoring the effect of reconfiguring services • monitoring the effect of introducing 7-day working • monitoring the impact of service decommissioning. Investigating stroke rates and comorbidities using the HES data will be beneficial by: • reducing the burden of data collection, • helping identify key areas for quality improvement • reducing unwarranted variation • enabling a broader understanding of comorbidity and its impact of the receipt of key processes of care and patient outcomes.
ROYAL COLLEGE OF PHYSICIANS OF LONDON	ROYAL COLLEGE OF PHYSICIANS OF LONDON	MRIS - Cause of Death Report	Identifiable	Sensitive	Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)	Ongoing	Y	ONS: The Royal College of Physicians (RCP) is the data processor responsible for producing the CCG Outcomes Indicator Set (CCGOIS) measure of mortality at 30 days for stroke patients. These results are provided to NHS Digital to publish as part of the wider CCGOIS. The results are also provided at team level to provide necessary context on the performance of clinical teams treating stroke patients. As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. It is also important that Royal College of Physicians are able to provide the information back to the clinical teams who have treated the patients. HES: The HES dataset is used to determine the case ascertainment (case ascertainment is a measure of the number of cases reported in the audit, compared to the number of cases identified in HES) of participants of the Sentinel Stroke National Audit Programme (SSNAP), that is, the proportion of coded stroke patients which are recorded in the audit; and identify any readmissions and further strokes, in order to compare quality of care with outcomes for patients. As the outputs of analysis of SSNAP are reported and publically available, the proportion of patients entered into the audit for each hospital team, compared with the numbers in HES, is vital in determining how results are used (for instance, if there is low case ascertainment, the mortality outcomes would not be reported so that there is no potential misrepresentation).	RCP will send cohort information to NHS Digital for linkage, they send NHS Number, Full postcode, Name, and a unique SSNAP ID. As part of the section 251 support, there is a method by which the information is sent to NHS Digital for linkage without the RCP viewing any patient identifiable information. NHS Digital return; • Non sensitive pseudonymised HES data with SSNAP ID for patients in cohort • Non sensitive pseudonymised HES data for patients with a diagnosis of stroke • Identifiable ONS date and cause of death RCP combine HES and ONS data with SSNAP data and combine into separate databases; one with SSNAP and ONS data and the other with SSNAP and HES data. Identifiers are held separately to other data and the pseudonym SSNAP ID is used to identify individual patients. With the exception of date of death, analysts access no identifiers. Pseudonymised HES Data is then analysed to calculate case ascertainment information for the audit. HES data is also used to validate some of the information collected in the audit. No HES data is stored or processed by Netsolving, all processing is undertaken at the RCP. Identifiable ONS data is analysed to produce 30 day mortality at CCG level and stroke team level (team usually equates to a hospital). Cause of death is used to disaggregate stroke specific deaths and deaths from other causes. For statistical purposes such as monitoring trends identifiable ONS death data is also passed back (via the secure webtool hosted by Netsolving) to registered individuals at participating trusts whereby they can access date of death for patients they submit to the audit. All arrangements for 3rd party access will be controlled through sublicensing agreements and will be for the benefit of health and care; all arrangements will be approved by the HSCIC before data being sent. All individuals with access to the data are substantive employees of the Royal College of Physicians of London.	ONS and HES: Indicators will be produced showing the performance of organisations and at national level for the purpose of monitoring and quality improvement, in particular: • Mortality within 30 days of hospital admission for stroke CCG Outcomes Indictor Set (CCGOIS) at least annually (first publication on 17 December 2014, next publication anticipated to be published by the end of 2017, subject to timely receipt of ONS data from NHS Digital) • Mortality within 30 days of hospital admission for stroke Team-level mortality results (published in line with CCGOIS and used for contextualising the results). (Team usually equates to a hospital). • Audit case ascertainment information • For statistical purposes such as monitoring trends registered individuals at Trusts can access date of death for patients they submit to the audit derived from ONS mortality data. • As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates and causes of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. • Planned analyses following agreement with our steering group investigating stroke rates and associations with comorbidities recorded in HES to be published in peer-reviewed journals	Case ascertainment information will be used to target trusts who are not achieving good levels of audit case ascertainment, this leads to more complete data and more valid results in future audit. Complete audit information is essential for service improvement, and improvements to stroke patient care. HES data is utilised to compare the number of records submitted to the audit, with the number recorded in HES. SSNAP are currently finalising the methodology for determining the number of new stroke admissions per team, which will then be used to better triangulate which teams are submitting fewer patients to the audit than they are recording in HES. The previous years’ HES denominators enabled the audit to target teams with poorer case ascertainment and to chase those teams before submission deadlines in order to maintain the national case ascertainment above 90%. In the latest round of reporting (for Aug-Nov 2016), it was estimated that 90% of routinely admitting teams in England and Wales submitted over 90% of their stroke cases to the audit. The percentage case ascertainment at teams is used to penalise teams with low case ascertainment, and functions as a key driver for high participation levels. This ongoing process is a key driver to high case ascertainment in the audit – comparing teams to HES has yielded a consistent 90%+ case ascertainment nationally, which is key to ensuring representativeness and usefulness of the audit data. The benefit of this the receipt of HES data is therefore to enable a useful audit dataset, and the use of previous years’ HES has ensured the audit’s reputation as a high quality data source, and enabled audit data to be used to highlight areas of unwarranted variation across the country, to identify key areas for improvement, and for use in a number of parliamentary questions. Mortality within 30 days of hospital admission is part of Domain 1 of the NHS CCG Outcome indicator set “reducing premature mortality”. CCGs will access the published information and use it to improve services through identification of good and bad practice. This will be of benefit both in terms of better value for money and better patient outcomes. Mortality at CCG level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. The CCG OIS mortality measure was last published by SSNAP and by NHS Digital in early 2017. CCG outliers were informed in late 2016 and conversations with the CCG chairs, and medical directors ensued. One CCG was flagged as an outlier, despite none of its constituent hospitals being flagged. This raised a question to be investigated by the CCG as to whether they needed to assess their commissioning of services. The analyses will also help CCGs and STPs in the debate around where services should be reconfigured by enabling the use of appropriately adjusted mortality information. Outlier CCGs will again be contacted using an outlier processes to discuss where improvements in stroke care are needed in order to benefit both in terms of better value for money and better patient outcomes. The data will be published on the SSNAP website, as well as part of the CCG OIS, and is normally considered for other outputs such as PHE’s CVD profiles, Atlas of Variation etc. Reporting and publishing this information in the future is key to ensuring CCGs with high mortality rates are informed of this, and have the opportunity to improve. Similarly, trusts will use team level mortality within 30 days of hospital admission to identify trends and good practice, again leading to better patient outcomes. Mortality at team level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. In the past year, the mortality outlier for 2015-16 requested a stroke peer review visit to help identify key ways to improve their service. A quality improvement programme is now being implemented in this service, following from a detailed peer review visit. Outlier teams will again be contacted using an outlier processes to discuss where improvements in stroke care are needed with the Chief Executive, medical director and clinical lead for stroke. This information will be put into the public domain so patients and the public can see which hospitals have poor outcomes, for example through the MyNHS website. A stroke peer review visit will be offered to outlying teams to assist with identifying key areas for improvement and ways to achieve that improvement. Feeding back mortality information to teams, allows teams to investigate their patient outcomes and put in place ways to improve, for example by investigating patient deaths following the use of thrombolysis. Statistical analyses investigating longer-term mortality will have the following benefits: • assessing the real-world benefit of new interventions such as intra-arterial intervention (mechanical thrombectomy) and intermittent pneumatic compression stockings • tracking changes in mortality trends over time • monitoring the effect of reconfiguring services • monitoring the effect of introducing 7-day working • monitoring the impact of service decommissioning. Investigating stroke rates and comorbidities using the HES data will be beneficial by: • reducing the burden of data collection, • helping identify key areas for quality improvement • reducing unwarranted variation • enabling a broader understanding of comorbidity and its impact of the receipt of key processes of care and patient outcomes.
ROYAL COLLEGE OF PHYSICIANS OF LONDON	ROYAL COLLEGE OF PHYSICIANS OF LONDON	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	ONS: The Royal College of Physicians (RCP) is the data processor responsible for producing the CCG Outcomes Indicator Set (CCGOIS) measure of mortality at 30 days for stroke patients. These results are provided to NHS Digital to publish as part of the wider CCGOIS. The results are also provided at team level to provide necessary context on the performance of clinical teams treating stroke patients. As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. It is also important that Royal College of Physicians are able to provide the information back to the clinical teams who have treated the patients. HES: The HES dataset is used to determine the case ascertainment (case ascertainment is a measure of the number of cases reported in the audit, compared to the number of cases identified in HES) of participants of the Sentinel Stroke National Audit Programme (SSNAP), that is, the proportion of coded stroke patients which are recorded in the audit; and identify any readmissions and further strokes, in order to compare quality of care with outcomes for patients. As the outputs of analysis of SSNAP are reported and publically available, the proportion of patients entered into the audit for each hospital team, compared with the numbers in HES, is vital in determining how results are used (for instance, if there is low case ascertainment, the mortality outcomes would not be reported so that there is no potential misrepresentation).	RCP will send cohort information to NHS Digital for linkage, they send NHS Number, Full postcode, Name, and a unique SSNAP ID. As part of the section 251 support, there is a method by which the information is sent to NHS Digital for linkage without the RCP viewing any patient identifiable information. NHS Digital return; • Non sensitive pseudonymised HES data with SSNAP ID for patients in cohort • Non sensitive pseudonymised HES data for patients with a diagnosis of stroke • Identifiable ONS date and cause of death RCP combine HES and ONS data with SSNAP data and combine into separate databases; one with SSNAP and ONS data and the other with SSNAP and HES data. Identifiers are held separately to other data and the pseudonym SSNAP ID is used to identify individual patients. With the exception of date of death, analysts access no identifiers. Pseudonymised HES Data is then analysed to calculate case ascertainment information for the audit. HES data is also used to validate some of the information collected in the audit. No HES data is stored or processed by Netsolving, all processing is undertaken at the RCP. Identifiable ONS data is analysed to produce 30 day mortality at CCG level and stroke team level (team usually equates to a hospital). Cause of death is used to disaggregate stroke specific deaths and deaths from other causes. For statistical purposes such as monitoring trends identifiable ONS death data is also passed back (via the secure webtool hosted by Netsolving) to registered individuals at participating trusts whereby they can access date of death for patients they submit to the audit. All arrangements for 3rd party access will be controlled through sublicensing agreements and will be for the benefit of health and care; all arrangements will be approved by the HSCIC before data being sent. All individuals with access to the data are substantive employees of the Royal College of Physicians of London.	ONS and HES: Indicators will be produced showing the performance of organisations and at national level for the purpose of monitoring and quality improvement, in particular: • Mortality within 30 days of hospital admission for stroke CCG Outcomes Indictor Set (CCGOIS) at least annually (first publication on 17 December 2014, next publication anticipated to be published by the end of 2017, subject to timely receipt of ONS data from NHS Digital) • Mortality within 30 days of hospital admission for stroke Team-level mortality results (published in line with CCGOIS and used for contextualising the results). (Team usually equates to a hospital). • Audit case ascertainment information • For statistical purposes such as monitoring trends registered individuals at Trusts can access date of death for patients they submit to the audit derived from ONS mortality data. • As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates and causes of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. • Planned analyses following agreement with our steering group investigating stroke rates and associations with comorbidities recorded in HES to be published in peer-reviewed journals	Case ascertainment information will be used to target trusts who are not achieving good levels of audit case ascertainment, this leads to more complete data and more valid results in future audit. Complete audit information is essential for service improvement, and improvements to stroke patient care. HES data is utilised to compare the number of records submitted to the audit, with the number recorded in HES. SSNAP are currently finalising the methodology for determining the number of new stroke admissions per team, which will then be used to better triangulate which teams are submitting fewer patients to the audit than they are recording in HES. The previous years’ HES denominators enabled the audit to target teams with poorer case ascertainment and to chase those teams before submission deadlines in order to maintain the national case ascertainment above 90%. In the latest round of reporting (for Aug-Nov 2016), it was estimated that 90% of routinely admitting teams in England and Wales submitted over 90% of their stroke cases to the audit. The percentage case ascertainment at teams is used to penalise teams with low case ascertainment, and functions as a key driver for high participation levels. This ongoing process is a key driver to high case ascertainment in the audit – comparing teams to HES has yielded a consistent 90%+ case ascertainment nationally, which is key to ensuring representativeness and usefulness of the audit data. The benefit of this the receipt of HES data is therefore to enable a useful audit dataset, and the use of previous years’ HES has ensured the audit’s reputation as a high quality data source, and enabled audit data to be used to highlight areas of unwarranted variation across the country, to identify key areas for improvement, and for use in a number of parliamentary questions. Mortality within 30 days of hospital admission is part of Domain 1 of the NHS CCG Outcome indicator set “reducing premature mortality”. CCGs will access the published information and use it to improve services through identification of good and bad practice. This will be of benefit both in terms of better value for money and better patient outcomes. Mortality at CCG level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. The CCG OIS mortality measure was last published by SSNAP and by NHS Digital in early 2017. CCG outliers were informed in late 2016 and conversations with the CCG chairs, and medical directors ensued. One CCG was flagged as an outlier, despite none of its constituent hospitals being flagged. This raised a question to be investigated by the CCG as to whether they needed to assess their commissioning of services. The analyses will also help CCGs and STPs in the debate around where services should be reconfigured by enabling the use of appropriately adjusted mortality information. Outlier CCGs will again be contacted using an outlier processes to discuss where improvements in stroke care are needed in order to benefit both in terms of better value for money and better patient outcomes. The data will be published on the SSNAP website, as well as part of the CCG OIS, and is normally considered for other outputs such as PHE’s CVD profiles, Atlas of Variation etc. Reporting and publishing this information in the future is key to ensuring CCGs with high mortality rates are informed of this, and have the opportunity to improve. Similarly, trusts will use team level mortality within 30 days of hospital admission to identify trends and good practice, again leading to better patient outcomes. Mortality at team level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. In the past year, the mortality outlier for 2015-16 requested a stroke peer review visit to help identify key ways to improve their service. A quality improvement programme is now being implemented in this service, following from a detailed peer review visit. Outlier teams will again be contacted using an outlier processes to discuss where improvements in stroke care are needed with the Chief Executive, medical director and clinical lead for stroke. This information will be put into the public domain so patients and the public can see which hospitals have poor outcomes, for example through the MyNHS website. A stroke peer review visit will be offered to outlying teams to assist with identifying key areas for improvement and ways to achieve that improvement. Feeding back mortality information to teams, allows teams to investigate their patient outcomes and put in place ways to improve, for example by investigating patient deaths following the use of thrombolysis. Statistical analyses investigating longer-term mortality will have the following benefits: • assessing the real-world benefit of new interventions such as intra-arterial intervention (mechanical thrombectomy) and intermittent pneumatic compression stockings • tracking changes in mortality trends over time • monitoring the effect of reconfiguring services • monitoring the effect of introducing 7-day working • monitoring the impact of service decommissioning. Investigating stroke rates and comorbidities using the HES data will be beneficial by: • reducing the burden of data collection, • helping identify key areas for quality improvement • reducing unwarranted variation • enabling a broader understanding of comorbidity and its impact of the receipt of key processes of care and patient outcomes.
ROYAL COLLEGE OF PHYSICIANS OF LONDON	ROYAL COLLEGE OF PHYSICIANS OF LONDON	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	One-Off	Y	ONS: The Royal College of Physicians (RCP) is the data processor responsible for producing the CCG Outcomes Indicator Set (CCGOIS) measure of mortality at 30 days for stroke patients. These results are provided to NHS Digital to publish as part of the wider CCGOIS. The results are also provided at team level to provide necessary context on the performance of clinical teams treating stroke patients. As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. It is also important that Royal College of Physicians are able to provide the information back to the clinical teams who have treated the patients. HES: The HES dataset is used to determine the case ascertainment (case ascertainment is a measure of the number of cases reported in the audit, compared to the number of cases identified in HES) of participants of the Sentinel Stroke National Audit Programme (SSNAP), that is, the proportion of coded stroke patients which are recorded in the audit; and identify any readmissions and further strokes, in order to compare quality of care with outcomes for patients. As the outputs of analysis of SSNAP are reported and publically available, the proportion of patients entered into the audit for each hospital team, compared with the numbers in HES, is vital in determining how results are used (for instance, if there is low case ascertainment, the mortality outcomes would not be reported so that there is no potential misrepresentation).	RCP will send cohort information to NHS Digital for linkage, they send NHS Number, Full postcode, Name, and a unique SSNAP ID. As part of the section 251 support, there is a method by which the information is sent to NHS Digital for linkage without the RCP viewing any patient identifiable information. NHS Digital return; • Non sensitive pseudonymised HES data with SSNAP ID for patients in cohort • Non sensitive pseudonymised HES data for patients with a diagnosis of stroke • Identifiable ONS date and cause of death RCP combine HES and ONS data with SSNAP data and combine into separate databases; one with SSNAP and ONS data and the other with SSNAP and HES data. Identifiers are held separately to other data and the pseudonym SSNAP ID is used to identify individual patients. With the exception of date of death, analysts access no identifiers. Pseudonymised HES Data is then analysed to calculate case ascertainment information for the audit. HES data is also used to validate some of the information collected in the audit. No HES data is stored or processed by Netsolving, all processing is undertaken at the RCP. Identifiable ONS data is analysed to produce 30 day mortality at CCG level and stroke team level (team usually equates to a hospital). Cause of death is used to disaggregate stroke specific deaths and deaths from other causes. For statistical purposes such as monitoring trends identifiable ONS death data is also passed back (via the secure webtool hosted by Netsolving) to registered individuals at participating trusts whereby they can access date of death for patients they submit to the audit. All arrangements for 3rd party access will be controlled through sublicensing agreements and will be for the benefit of health and care; all arrangements will be approved by the HSCIC before data being sent. All individuals with access to the data are substantive employees of the Royal College of Physicians of London.	ONS and HES: Indicators will be produced showing the performance of organisations and at national level for the purpose of monitoring and quality improvement, in particular: • Mortality within 30 days of hospital admission for stroke CCG Outcomes Indictor Set (CCGOIS) at least annually (first publication on 17 December 2014, next publication anticipated to be published by the end of 2017, subject to timely receipt of ONS data from NHS Digital) • Mortality within 30 days of hospital admission for stroke Team-level mortality results (published in line with CCGOIS and used for contextualising the results). (Team usually equates to a hospital). • Audit case ascertainment information • For statistical purposes such as monitoring trends registered individuals at Trusts can access date of death for patients they submit to the audit derived from ONS mortality data. • As well as reporting on 30 day mortality, there is a need to show survival at other intervals such as at 6 months and 1 year. The outputs of the analysis by RCP will include mortality statistics at different time points and at different levels of granularity and dates and causes of death will be used in statistical modelling. Any data reported on is carefully considered in terms of whether it could be potentially identifiable and advice is given on how the outputs should be interpreted. • Planned analyses following agreement with our steering group investigating stroke rates and associations with comorbidities recorded in HES to be published in peer-reviewed journals	Case ascertainment information will be used to target trusts who are not achieving good levels of audit case ascertainment, this leads to more complete data and more valid results in future audit. Complete audit information is essential for service improvement, and improvements to stroke patient care. HES data is utilised to compare the number of records submitted to the audit, with the number recorded in HES. SSNAP are currently finalising the methodology for determining the number of new stroke admissions per team, which will then be used to better triangulate which teams are submitting fewer patients to the audit than they are recording in HES. The previous years’ HES denominators enabled the audit to target teams with poorer case ascertainment and to chase those teams before submission deadlines in order to maintain the national case ascertainment above 90%. In the latest round of reporting (for Aug-Nov 2016), it was estimated that 90% of routinely admitting teams in England and Wales submitted over 90% of their stroke cases to the audit. The percentage case ascertainment at teams is used to penalise teams with low case ascertainment, and functions as a key driver for high participation levels. This ongoing process is a key driver to high case ascertainment in the audit – comparing teams to HES has yielded a consistent 90%+ case ascertainment nationally, which is key to ensuring representativeness and usefulness of the audit data. The benefit of this the receipt of HES data is therefore to enable a useful audit dataset, and the use of previous years’ HES has ensured the audit’s reputation as a high quality data source, and enabled audit data to be used to highlight areas of unwarranted variation across the country, to identify key areas for improvement, and for use in a number of parliamentary questions. Mortality within 30 days of hospital admission is part of Domain 1 of the NHS CCG Outcome indicator set “reducing premature mortality”. CCGs will access the published information and use it to improve services through identification of good and bad practice. This will be of benefit both in terms of better value for money and better patient outcomes. Mortality at CCG level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. The CCG OIS mortality measure was last published by SSNAP and by NHS Digital in early 2017. CCG outliers were informed in late 2016 and conversations with the CCG chairs, and medical directors ensued. One CCG was flagged as an outlier, despite none of its constituent hospitals being flagged. This raised a question to be investigated by the CCG as to whether they needed to assess their commissioning of services. The analyses will also help CCGs and STPs in the debate around where services should be reconfigured by enabling the use of appropriately adjusted mortality information. Outlier CCGs will again be contacted using an outlier processes to discuss where improvements in stroke care are needed in order to benefit both in terms of better value for money and better patient outcomes. The data will be published on the SSNAP website, as well as part of the CCG OIS, and is normally considered for other outputs such as PHE’s CVD profiles, Atlas of Variation etc. Reporting and publishing this information in the future is key to ensuring CCGs with high mortality rates are informed of this, and have the opportunity to improve. Similarly, trusts will use team level mortality within 30 days of hospital admission to identify trends and good practice, again leading to better patient outcomes. Mortality at team level using a casemix-adjusted model has been reported for 3 years now, for 2013/14, 2014/15 and for 2015/16. In the past year, the mortality outlier for 2015-16 requested a stroke peer review visit to help identify key ways to improve their service. A quality improvement programme is now being implemented in this service, following from a detailed peer review visit. Outlier teams will again be contacted using an outlier processes to discuss where improvements in stroke care are needed with the Chief Executive, medical director and clinical lead for stroke. This information will be put into the public domain so patients and the public can see which hospitals have poor outcomes, for example through the MyNHS website. A stroke peer review visit will be offered to outlying teams to assist with identifying key areas for improvement and ways to achieve that improvement. Feeding back mortality information to teams, allows teams to investigate their patient outcomes and put in place ways to improve, for example by investigating patient deaths following the use of thrombolysis. Statistical analyses investigating longer-term mortality will have the following benefits: • assessing the real-world benefit of new interventions such as intra-arterial intervention (mechanical thrombectomy) and intermittent pneumatic compression stockings • tracking changes in mortality trends over time • monitoring the effect of reconfiguring services • monitoring the effect of introducing 7-day working • monitoring the impact of service decommissioning. Investigating stroke rates and comorbidities using the HES data will be beneficial by: • reducing the burden of data collection, • helping identify key areas for quality improvement • reducing unwarranted variation • enabling a broader understanding of comorbidity and its impact of the receipt of key processes of care and patient outcomes.
SAVING FACES	SAVING FACES	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The role of selective neck dissection used electively in patients with early oral squamous cell carcinoma (1−3cm primary size) and no clinical evidence of lymph node metastases in the neck (N0) The trial aims to establish whether selective neck dissection in addition to resection of the primary tumour increases survival when compared with resection of the primary alone, among patients with early oral squamous cell carcinoma who have no clinical or radiological evidence of neck metastases. Additionally the trial aims to determine how SEND and complex reconstruction affect quality of life (QOL) and whether the use of SEND on all patients presenting with tumors measuring 1-3cms and clinically N0 necks represents a cost-effective use of resources. We will also use the pathology results to try to identify risk factors for metastasis so that in the future elective neck dissection could be reserved for only those patients with an increased risk of neck metastasis based on their primary site pathological markers. Data access is restricted to those named in section 7 of this agreement. Any changes will be notified to the NHS IC.
SAVING FACES	SAVING FACES	MRIS - Cohort Event Notification Report	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	The role of selective neck dissection used electively in patients with early oral squamous cell carcinoma (1−3cm primary size) and no clinical evidence of lymph node metastases in the neck (N0) The trial aims to establish whether selective neck dissection in addition to resection of the primary tumour increases survival when compared with resection of the primary alone, among patients with early oral squamous cell carcinoma who have no clinical or radiological evidence of neck metastases. Additionally the trial aims to determine how SEND and complex reconstruction affect quality of life (QOL) and whether the use of SEND on all patients presenting with tumors measuring 1-3cms and clinically N0 necks represents a cost-effective use of resources. We will also use the pathology results to try to identify risk factors for metastasis so that in the future elective neck dissection could be reserved for only those patients with an increased risk of neck metastasis based on their primary site pathological markers. Data access is restricted to those named in section 7 of this agreement. Any changes will be notified to the NHS IC.
THE HEALTH FOUNDATION	THE HEALTH FOUNDATION	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The Health Foundation is an independent charity working to improve health and the quality of health care in the United Kingdom. The Health Foundation is requesting access to HES data for the “Assessment of inequality” project to inform public discussions about the focus, design and effects of policies intended to improve the quality of health care in the United Kingdom. The project will inform policymakers and the NHS about the variability in health inequality with respect to NHS hospital services and A&E waiting times in England by geographical area and ethnicity, and thus help to identify priority areas for reducing inequality. The aim of the project is to identify areas (geographical and within treatment specialities) in which healthcare inequality exists. The Health Foundation will disseminate these findings with the aim of raising awareness of existing and growing inequalities in access to healthcare and how these have changed over time and also to inform policy makers of areas in which there is potential for these inequalities to be redressed. The Health Foundation meets regularly with representatives from Department of Health, NHS England and NHS Improvement and the findings will inform ongoing conversations and interim findings will be presented to these representatives. The Health Foundation will also engage with patient advocacy groups to ensure that the findings will benefit NHS hospital patients. Objective: (i) The objective of this project is to create an evidence base that will inform policymakers in the Department of Health, NHS England and NHS Improvement about the variability in health inequality with respect to NHS hospital services and A&E waiting times in England by treatment speciality, geographical area and ethnicity. (ii) The Health Foundation will also assess health inequality within maternity services due to the topical nature of this issue and the high level of media coverage in recent years including issues identified at Morecambe Bay maternity (Bunyan, 2015). (iii) The project will also create an economics model of the determinant of health inequality by geographical area, deprivation and ethnicity. It is recognised in the literature that health inequality exists in England for example Cookson et al. (2016) highlights that residents of more deprived areas are more likely to die from treatable conditions and less likely to see a specialist than residents of less deprived areas. England is not alone in the existence of health inequality; Hart & Williams (2009) also discussed the presence of health inequality in the American setting, linking inequality to quality of life as well as health service factors such as access to care and quality of care. The NHS has a mandate from the government to reduce health inequality (Department of Health, 2015). One reason that this analysis aims to investigate health inequality in England is to assess how the health inequality level has changed in recent years, as the NHS has faced rising financial difficulty, and whether it can be expected to reduce in future years in line with the NHS’ mandate. The Health Foundation would also like to assess health inequality within maternity services due to the topical nature of this issue and the high level of media coverage in recent years including issues identified at Morecambe Bay maternity (Bunyan, 2015). The Health Foundation will use hospital episode statistics (HES) data at pseudonymised patient level to assess the relationship between geographical area and inequality using a measure of inequality such as the slope index of inequality or the relative index of inequality. Trends in inequality will be examined in the years before the current period of austerity (2003-2010) and in the current period of austerity (2010-date). The Health Foundation require a sufficient length of time to reliably compare the changes in the trends during these two periods and examine the significant differences between the intervals. This is needed in order to adequately capture the impact of austerity on inequality in healthcare. The reason The Health Foundation wish to examine these differences is based on the theory that before austerity there will have been more resources available to put towards access to health care and to allocate these resources in the most equal way may have been easier during this period than the current period of austerity. Austerity aims to reduce deficits using methods such as reducing expenditure to bring it in line with revenue. Austerity is often associated with a reduction in government spending; in times of austerity this may result in spending cuts within certain hospital departments. The Health Foundation wish to investigate whether certain groups of the population are disproportionally affected by these cuts in comparison to others.	The requested data will be processed by a limited number of analysts within the Health Foundation’s Secure Data Environment (SDE). All researchers with access to the SDE will have completed an accreditation course on data protection legislation and statistical disclosure control, completed an information security training specific to the Health Foundation’s infrastructure, and signed a non-disclosure agreement and the terms of use of the secure environment. All researchers with access to the data are substantive employees of the Health Foundation. The data will only be processed on the Health Foundation’s premises on 90 Long Acre in London and any publication derived from the data will be aggregated with small numbers supressed in line with the NHS guidance before being released from the environment. As outlined in the objectives above, The Health Foundation would like to evaluate health inequality over time, and across different geographies within England. The aim is to investigate how changes in inequality in the most recent decade compares with changes in the preceding decade. There is a particular interest in trends in inequality and access to health care in the period before the current period of austerity and trends during the current period of austerity. For the purpose of this project The Health Foundation will consider the following periods: • 2003/04 until 2009/10 as the pre-austerity period; • 2010, as the start of the austerity; • 2010/11 onwards as the austerity period. For that purpose, the following comprehensive datasets are required: • Inpatients from 2003/04 to date • Outpatients from 2003/04 to date • A&E from 2007/08 to date For this project, The Health Foundation will combine inpatient, outpatient and A&E records over time to establish patients’ health care utilisation. The data will be further enhanced by linking in additional contextual information at GP practice level, or small area level (note: The Health Foundation will NOT link any data at patient level). Contextual data sources are typically publicly available statistics published by the Office for National Statistics, NHS Digital or NHS England (e.g. aggregated census data at small area level, GP patient survey data aggregated at GP level). As stated in the methodology, The Health Foundation wish to examine the differences in the changes in inequality over the period running up to austerity compared to during the current period of austerity. It is essential to this project to analyse data from all available years pre-austerity (from 2003/04) in order to capture all significant changes in health inequality within hospital departments in the period running up to austerity for an accurate comparison with the period of austerity. As A&E data are only available from 2007 onwards, this will restrict the analysis to a 3 year period before the period of austerity. However, The Health Foundation are expecting to complement this trend with underlying trends in hospital admissions and outpatients therefore it is imperative to this project to examine trends in inequality before austerity using the earliest available data for inpatients and outpatients (from 2003/04). The data will be used to generate descriptive statistics on inequality across different health care services, geographies within England, ethnicity and over time in line with objectives (i) and (ii). Measures of inequality that will be used include the slope index and the relative index of inequality. Objective (iii) will require the Health Foundation to apply statistical modelling to the same data, explaining inequality levels in terms of prevalence of health conditions, age, sex, location of residence, ethnicity and time.	As outlined above, outputs will be aggregated with small numbers supressed in line with the HES analysis guide. Results from the analysis will include: • Summary statistics (including number of observations, mean values and standard deviation values) for inequality regression analyses assessing health inequality in relation to geographical area, deprivation and ethnicity; • Estimated regression coefficients (and their associated p-values, which show the level of statistical significance of the estimated coefficient.) relating to the associations between health inequality and geographical area, deprivation and ethnicity; • Charts and maps to show changes in health inequality by geographical area, deprivation and ethnicity at the Trust or local authority level between 2003/04 and 2016/17; • The results of the regressions included in this analysis will be presented in a tabular format with an accompanying body of text describing and explaining the results. Planned outputs for this body of work are: (a) A Health Foundation report, similar to the recent report on the need for a dedicated transformation fund, Making change possible: a Transformation Fund for the NHS (the Health Foundation and the King’s Fund, 2015) ( https://www.kingsfund.org.uk/press/press-releases/making-change-happen-transformation-fund-nh). This will be accompanied by a detailed communications plan on how to target the media to ensure the results have the maximum penetration. It is expected that this report will be published in the first quarter of 2018 (January – March 2018). (b) Findings will be submitted to international peer-reviewed journals. These will be a mixture of health services research journals (e.g. Health Services Research and Policy) and economics journals (e.g. the Journal of Health Economics). These journals are read by policy makers, nationally and internationally, who wish to identify and classify hospitals according to the level of quality of care that they provide. The expectation is to submit articles for peer review no later than March 2018. (c) Interim findings will be submitted to (and if accepted presented at) various conferences to seek early feedback and to allow improvement of the work. Conferences targeted include: The Health Economist’s Study Group at University of Aberdeen (June 2017) or London City University (January 2018)), NHS Providers annual conference (November 2017), NHS Confed annual conference (June 2017 or 2018) (d) Findings will be presented at The Health Foundation to statutory bodies such as the Department of Health, NHS England and NHS Improvement. The Health Foundation expect to invite the statutory bodies by June 2018. Note: The Health Foundation meets regularly with representatives from the Department of Health, NHS England and NHS Improvement. The proposed work will inform ongoing conversation with these organisations and interim findings will be presented to their representatives in addition to the more formal outputs listed above. Similarly, The Health Foundation will engage with patient advocacy groups in order to ensure findings that may benefit NHS hospital patients, are communicated with these groups who can use them towards positive change.	Benefits In line with the primary objectives described above, the purpose of this project is to build an evidence base on inequality across England. This information will be used by commissioners including NHS England to identify priority areas for reducing inequality, in line with the aforementioned mandate from Government. The overall benefit of this work is that policymakers and commissioners can use the findings in targeting specific cohorts in the population where inequality is particularly high. Existing initiatives aimed at reducing inequality can be assessed, and better evidence will inform debates on inequality more widely. Specifically, objectives (i) and (ii) will inform the varying level of inequality across geographical areas and difference health care services (objective (ii) focussing primarily on the latter). Variation in inequality by health care service will help target commissioners in tackling the problem of inequality. Objective (iii) will model the cost of care for patients that are more or less deprived of access to health care (e.g. the average cost of an outpatient appointment may be higher for hard to reach groups). This will benefit commissioners and policy makers in prioritising this particular policy area. As described in the outputs section, outputs (a) and (d), as well as ongoing interaction with the Department of Health, NHS England and NHS Improvement will deliver the benefit for policy makers and commissioners, and in turn for patients. Outputs (b), (c) and the involvement of patient advocacy groups are targeted towards our objective to inform the public debate about health inequality in England. Note: the purpose of this project is to identify areas for improvement regarding inequality in access to health care services including hospital services, maternity services and access to A&E. Although the Health Foundation can help inform proposals to reduce inequality going forward, this project will not reduce inequality in and of itself. Audience As a non-profit organisation, the Health Foundation’s mission is to maximise the public benefit and the impact of the research that is produced in-house. The aim is to produce useful evidence that can inform better policy and ultimately improve health and health care. This is why The Health Foundation target specific areas of interest for policy and NHS users that are less explored and particularly complex to analyse. This is the case of health inequalities. The Health Foundation has strong links with NHS teams, national policymakers (e.g., NHS England) and patient advocacy groups. Examples of these links for previous projects are: • Senior members of Health Foundation staff regularly meet with senior representatives from across government, including the Treasury, Department of Health and Arms-Length Bodies (e.g. Monitor, CQC, NHS England, HEE). • The Health Foundation is currently working on joint projects with NHS organisations. One example is our partnership with NHS England in evaluating new models of care outlined in the Five Year Forward view (http://www.health.org.uk/programmes/projects/improvement-analytics-unit). • People across the Health Foundation regularly engage with policy makers at all levels on a range of topics where we have particular expertise: policy, data analytics, economics, patient safety and person-centred care. Health Foundation views are regularly sought on health policy and practice, meaning that the findings from these HES-based analyses will be communicated directly with policymakers. One example is the recent engagement of the Economics team with NHS Wales (http://www.health.org.uk/programmes/projects/fiscal-sustainability-nhs-wales), leading to the following report (http://www.health.org.uk/publication/path-sustainability). • The Health Foundation have a long history of funding programmes across the NHS which help to improve the quality of health care. For example, funding work on the relationship between patient flow, costs and outcomes in two NHS hospital trusts, which is related to the new project on understanding the drivers of A&E attendances. • The Health Foundation have an active audience of professionals working in the NHS, many of whom are fellows sponsored by the Health Foundation, award-holders or part of our alumni. Engagement The approach to dissemination includes not only publications but also active engagement with national policy makers, practitioners and researchers. This project will have a member of the Foundation’s Communications team leading on dissemination of findings which will include a number of alternative channels as TV, radio interviews and articles on national, local and online media.
THE HEALTH FOUNDATION	THE HEALTH FOUNDATION	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The Health Foundation is an independent charity working to improve health and the quality of health care in the United Kingdom. The Health Foundation is requesting access to HES data for the “Assessment of inequality” project to inform public discussions about the focus, design and effects of policies intended to improve the quality of health care in the United Kingdom. The project will inform policymakers and the NHS about the variability in health inequality with respect to NHS hospital services and A&E waiting times in England by geographical area and ethnicity, and thus help to identify priority areas for reducing inequality. The aim of the project is to identify areas (geographical and within treatment specialities) in which healthcare inequality exists. The Health Foundation will disseminate these findings with the aim of raising awareness of existing and growing inequalities in access to healthcare and how these have changed over time and also to inform policy makers of areas in which there is potential for these inequalities to be redressed. The Health Foundation meets regularly with representatives from Department of Health, NHS England and NHS Improvement and the findings will inform ongoing conversations and interim findings will be presented to these representatives. The Health Foundation will also engage with patient advocacy groups to ensure that the findings will benefit NHS hospital patients. Objective: (i) The objective of this project is to create an evidence base that will inform policymakers in the Department of Health, NHS England and NHS Improvement about the variability in health inequality with respect to NHS hospital services and A&E waiting times in England by treatment speciality, geographical area and ethnicity. (ii) The Health Foundation will also assess health inequality within maternity services due to the topical nature of this issue and the high level of media coverage in recent years including issues identified at Morecambe Bay maternity (Bunyan, 2015). (iii) The project will also create an economics model of the determinant of health inequality by geographical area, deprivation and ethnicity. It is recognised in the literature that health inequality exists in England for example Cookson et al. (2016) highlights that residents of more deprived areas are more likely to die from treatable conditions and less likely to see a specialist than residents of less deprived areas. England is not alone in the existence of health inequality; Hart & Williams (2009) also discussed the presence of health inequality in the American setting, linking inequality to quality of life as well as health service factors such as access to care and quality of care. The NHS has a mandate from the government to reduce health inequality (Department of Health, 2015). One reason that this analysis aims to investigate health inequality in England is to assess how the health inequality level has changed in recent years, as the NHS has faced rising financial difficulty, and whether it can be expected to reduce in future years in line with the NHS’ mandate. The Health Foundation would also like to assess health inequality within maternity services due to the topical nature of this issue and the high level of media coverage in recent years including issues identified at Morecambe Bay maternity (Bunyan, 2015). The Health Foundation will use hospital episode statistics (HES) data at pseudonymised patient level to assess the relationship between geographical area and inequality using a measure of inequality such as the slope index of inequality or the relative index of inequality. Trends in inequality will be examined in the years before the current period of austerity (2003-2010) and in the current period of austerity (2010-date). The Health Foundation require a sufficient length of time to reliably compare the changes in the trends during these two periods and examine the significant differences between the intervals. This is needed in order to adequately capture the impact of austerity on inequality in healthcare. The reason The Health Foundation wish to examine these differences is based on the theory that before austerity there will have been more resources available to put towards access to health care and to allocate these resources in the most equal way may have been easier during this period than the current period of austerity. Austerity aims to reduce deficits using methods such as reducing expenditure to bring it in line with revenue. Austerity is often associated with a reduction in government spending; in times of austerity this may result in spending cuts within certain hospital departments. The Health Foundation wish to investigate whether certain groups of the population are disproportionally affected by these cuts in comparison to others.	The requested data will be processed by a limited number of analysts within the Health Foundation’s Secure Data Environment (SDE). All researchers with access to the SDE will have completed an accreditation course on data protection legislation and statistical disclosure control, completed an information security training specific to the Health Foundation’s infrastructure, and signed a non-disclosure agreement and the terms of use of the secure environment. All researchers with access to the data are substantive employees of the Health Foundation. The data will only be processed on the Health Foundation’s premises on 90 Long Acre in London and any publication derived from the data will be aggregated with small numbers supressed in line with the NHS guidance before being released from the environment. As outlined in the objectives above, The Health Foundation would like to evaluate health inequality over time, and across different geographies within England. The aim is to investigate how changes in inequality in the most recent decade compares with changes in the preceding decade. There is a particular interest in trends in inequality and access to health care in the period before the current period of austerity and trends during the current period of austerity. For the purpose of this project The Health Foundation will consider the following periods: • 2003/04 until 2009/10 as the pre-austerity period; • 2010, as the start of the austerity; • 2010/11 onwards as the austerity period. For that purpose, the following comprehensive datasets are required: • Inpatients from 2003/04 to date • Outpatients from 2003/04 to date • A&E from 2007/08 to date For this project, The Health Foundation will combine inpatient, outpatient and A&E records over time to establish patients’ health care utilisation. The data will be further enhanced by linking in additional contextual information at GP practice level, or small area level (note: The Health Foundation will NOT link any data at patient level). Contextual data sources are typically publicly available statistics published by the Office for National Statistics, NHS Digital or NHS England (e.g. aggregated census data at small area level, GP patient survey data aggregated at GP level). As stated in the methodology, The Health Foundation wish to examine the differences in the changes in inequality over the period running up to austerity compared to during the current period of austerity. It is essential to this project to analyse data from all available years pre-austerity (from 2003/04) in order to capture all significant changes in health inequality within hospital departments in the period running up to austerity for an accurate comparison with the period of austerity. As A&E data are only available from 2007 onwards, this will restrict the analysis to a 3 year period before the period of austerity. However, The Health Foundation are expecting to complement this trend with underlying trends in hospital admissions and outpatients therefore it is imperative to this project to examine trends in inequality before austerity using the earliest available data for inpatients and outpatients (from 2003/04). The data will be used to generate descriptive statistics on inequality across different health care services, geographies within England, ethnicity and over time in line with objectives (i) and (ii). Measures of inequality that will be used include the slope index and the relative index of inequality. Objective (iii) will require the Health Foundation to apply statistical modelling to the same data, explaining inequality levels in terms of prevalence of health conditions, age, sex, location of residence, ethnicity and time.	As outlined above, outputs will be aggregated with small numbers supressed in line with the HES analysis guide. Results from the analysis will include: • Summary statistics (including number of observations, mean values and standard deviation values) for inequality regression analyses assessing health inequality in relation to geographical area, deprivation and ethnicity; • Estimated regression coefficients (and their associated p-values, which show the level of statistical significance of the estimated coefficient.) relating to the associations between health inequality and geographical area, deprivation and ethnicity; • Charts and maps to show changes in health inequality by geographical area, deprivation and ethnicity at the Trust or local authority level between 2003/04 and 2016/17; • The results of the regressions included in this analysis will be presented in a tabular format with an accompanying body of text describing and explaining the results. Planned outputs for this body of work are: (a) A Health Foundation report, similar to the recent report on the need for a dedicated transformation fund, Making change possible: a Transformation Fund for the NHS (the Health Foundation and the King’s Fund, 2015) ( https://www.kingsfund.org.uk/press/press-releases/making-change-happen-transformation-fund-nh). This will be accompanied by a detailed communications plan on how to target the media to ensure the results have the maximum penetration. It is expected that this report will be published in the first quarter of 2018 (January – March 2018). (b) Findings will be submitted to international peer-reviewed journals. These will be a mixture of health services research journals (e.g. Health Services Research and Policy) and economics journals (e.g. the Journal of Health Economics). These journals are read by policy makers, nationally and internationally, who wish to identify and classify hospitals according to the level of quality of care that they provide. The expectation is to submit articles for peer review no later than March 2018. (c) Interim findings will be submitted to (and if accepted presented at) various conferences to seek early feedback and to allow improvement of the work. Conferences targeted include: The Health Economist’s Study Group at University of Aberdeen (June 2017) or London City University (January 2018)), NHS Providers annual conference (November 2017), NHS Confed annual conference (June 2017 or 2018) (d) Findings will be presented at The Health Foundation to statutory bodies such as the Department of Health, NHS England and NHS Improvement. The Health Foundation expect to invite the statutory bodies by June 2018. Note: The Health Foundation meets regularly with representatives from the Department of Health, NHS England and NHS Improvement. The proposed work will inform ongoing conversation with these organisations and interim findings will be presented to their representatives in addition to the more formal outputs listed above. Similarly, The Health Foundation will engage with patient advocacy groups in order to ensure findings that may benefit NHS hospital patients, are communicated with these groups who can use them towards positive change.	Benefits In line with the primary objectives described above, the purpose of this project is to build an evidence base on inequality across England. This information will be used by commissioners including NHS England to identify priority areas for reducing inequality, in line with the aforementioned mandate from Government. The overall benefit of this work is that policymakers and commissioners can use the findings in targeting specific cohorts in the population where inequality is particularly high. Existing initiatives aimed at reducing inequality can be assessed, and better evidence will inform debates on inequality more widely. Specifically, objectives (i) and (ii) will inform the varying level of inequality across geographical areas and difference health care services (objective (ii) focussing primarily on the latter). Variation in inequality by health care service will help target commissioners in tackling the problem of inequality. Objective (iii) will model the cost of care for patients that are more or less deprived of access to health care (e.g. the average cost of an outpatient appointment may be higher for hard to reach groups). This will benefit commissioners and policy makers in prioritising this particular policy area. As described in the outputs section, outputs (a) and (d), as well as ongoing interaction with the Department of Health, NHS England and NHS Improvement will deliver the benefit for policy makers and commissioners, and in turn for patients. Outputs (b), (c) and the involvement of patient advocacy groups are targeted towards our objective to inform the public debate about health inequality in England. Note: the purpose of this project is to identify areas for improvement regarding inequality in access to health care services including hospital services, maternity services and access to A&E. Although the Health Foundation can help inform proposals to reduce inequality going forward, this project will not reduce inequality in and of itself. Audience As a non-profit organisation, the Health Foundation’s mission is to maximise the public benefit and the impact of the research that is produced in-house. The aim is to produce useful evidence that can inform better policy and ultimately improve health and health care. This is why The Health Foundation target specific areas of interest for policy and NHS users that are less explored and particularly complex to analyse. This is the case of health inequalities. The Health Foundation has strong links with NHS teams, national policymakers (e.g., NHS England) and patient advocacy groups. Examples of these links for previous projects are: • Senior members of Health Foundation staff regularly meet with senior representatives from across government, including the Treasury, Department of Health and Arms-Length Bodies (e.g. Monitor, CQC, NHS England, HEE). • The Health Foundation is currently working on joint projects with NHS organisations. One example is our partnership with NHS England in evaluating new models of care outlined in the Five Year Forward view (http://www.health.org.uk/programmes/projects/improvement-analytics-unit). • People across the Health Foundation regularly engage with policy makers at all levels on a range of topics where we have particular expertise: policy, data analytics, economics, patient safety and person-centred care. Health Foundation views are regularly sought on health policy and practice, meaning that the findings from these HES-based analyses will be communicated directly with policymakers. One example is the recent engagement of the Economics team with NHS Wales (http://www.health.org.uk/programmes/projects/fiscal-sustainability-nhs-wales), leading to the following report (http://www.health.org.uk/publication/path-sustainability). • The Health Foundation have a long history of funding programmes across the NHS which help to improve the quality of health care. For example, funding work on the relationship between patient flow, costs and outcomes in two NHS hospital trusts, which is related to the new project on understanding the drivers of A&E attendances. • The Health Foundation have an active audience of professionals working in the NHS, many of whom are fellows sponsored by the Health Foundation, award-holders or part of our alumni. Engagement The approach to dissemination includes not only publications but also active engagement with national policy makers, practitioners and researchers. This project will have a member of the Foundation’s Communications team leading on dissemination of findings which will include a number of alternative channels as TV, radio interviews and articles on national, local and online media.
THE INSTITUTE FOR FISCAL STUDIES (IFS)	THE INSTITUTE FOR FISCAL STUDIES (IFS)	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	Y	The Institute of Fiscal Studies (IFS) conducts independent research into the effects of economics on health and the health system with the aim to better inform policy makers, practitioners and the general public IFS have a number of research projects being conducted at once. This document numbers and describes each project aim, processing, outputs and benefits individually. (1) To understand whether and how patients exercise choice when there is no entry of private providers: the case of maternity. The aim is to address three questions: (i), are patients able to make a choice when there is very limited spare capacity in the system? (ii), if patients are able to exercise choice, what types of patients are able and willing to exert choice? (iii) do women use experience from their earlier maternities when making decisions about where and when to give birth to subsequent children? (2) To produce a model of choice that can be used to simulate and evaluate potential future policies. The focus will be on how potential policies affect where different types of patient (by age, location, or area level deprivation) are treated. (3) Comparing health care expenditure, activity and outcomes in the US and England, using the specific case study of percutaneous coronary intervention (PCI) treatment for acute myocardial infarction (AMI) patients aged 65 and over. (4) The objectives of this project are (i) to understand the impact of the introduction and expansion of the role of independent sector providers on demand for NHS-funded joint replacements and (ii) to assess how this impact varies across England, and the area level deprivation. (5) The objectives of this project are (i) to produce profiles of public-funded medical expenditure in England over the life cycle (and examine how this evolves over time), (ii) examine correlations in the concentration of medical spending over time (i.e. how much does spending on healthcare in a given year determine the amount of healthcare received in the future), and (iii) examine the share of medical spending attributed to patients in the last year of life. (6) To investigate how the demand for, and quality of NHS services have changed in areas where population has experienced rapid changes. In particular, IFS will examine whether areas with a high number or concentration of residents who are foreign born experience greater demand for two types of NHS services: (i) Accident and Emergency care and (ii) maternity services. (7) To estimate the health effects of Sure Start, a large national programme to improve early childhood development and integrate health, education, childcare, social care, and other support services to better serve families. The HES data will be used to: (i) investigate whether access to Sure Start services between birth and age 4 reduced all-cause and cause-specific hospitalisations and outpatient visits; (ii) understand the rollout of the Sure Start programme. This project can be completed with existing data. (8) To estimate the frequency of drug-related hospital admissions, focusing on cannabis-related hospital admissions, as well as admissions related to other drugs and alcohol, by region (especially TV region equivalents) and period (yearly and monthly), and in relation to demographic characteristics (such as age group and gender). These figures will then be compared to region and period specific estimates of the market size for cannabis, based on other data sources (especially sales data for tobacco-related products). This project can be completed with existing data. (9) To examine the variation in 30-day mortality rates of patient who are treated for AMI or stroke across different consultants and different hospitals. The focus will be to quantify the extent to which different consultants determine the probability of survival for patients, after taking into account the different characteristics of patients treated by different consultants, and the facilities available to consultants in each NHS hospital. This project requires the pconsult variable. IFS currently hold this variable for 2010/11 onwards, which came as part of the standard extract. This variable is required from 2003/04 to 2009/10. From May 2017 (10) IFS request linked HES-ONS mortality data to examine the impacts of the national four-hour waiting time target in NHS accident and emergency (A&E) departments. In particular IFS will examine three questions: a. Does the four-hour waiting time target change the probability of inpatient admission from A&E (e.g. are admission decisions distorted by the presence of the target)? b. What are the consequences for patient outcomes of changes in admission decisions? c. What are the consequences for the amount of resources used by hospitals due to changes in admission decisions? (11) To examine the effect of the policy shift towards choice and competition on the performance of UK acute hospital trusts. In particular the focus is on industry dynamics - which hospital trusts gained from choice and competition and which lost, and what impact did this have on the service and quality of care for their users and local populations. (12) The objective of this project is to estimate the effect of the UNICEF Baby Friendly Initiative on children’s health and health care use. The UNICEF Baby Friendly Initiative is a worldwide program that promotes breastfeeding through improving breastfeeding support services in hospitals and community services (i.e. health visiting teams). Improving breastfeeding might have effects on health and health care consumption. Although some benefits of breastfeeding are well recognised, the evidence on some other benefits is weaker. IFS will study whether the implementation of The UNICEF Baby Friendly Initiative in either a hospital or community service is associated with improvements in child and maternal health, as well as health care use. (13) The objective of this project is to understand whether hospitals that are more research intensive take up treatment innovations sooner, and whether this is translated in better health outcomes for patients and/or reduced costs for providers to achieve a given patient outcome. (14) The aim of this project to quantify the benefits of breastfeeding on children's health and cognitive development. Children born at weekends (or just before) might be less likely to be breastfed due to poorer breastfeeding support at the weekend. The project aims to use the variation in day of birth to set out the returns (in terms of patient health) to being breastfed. (15) The overall objective of the project is to evaluate how emergency admissions affect hospital production and patient outcomes in trauma and orthopaedic departments. There are three sub-objectives: (i) quantify how changes in emergency admissions have affected NHS hospitals across a range of outcomes including readmissions, cancellations of elective surgery, and length of stay; (ii) compare how the relationship between emergency admissions and these outcomes has changed in response to past NHS policies including Payment by Results, Referral To Treatment targets and NHS Choices; and (iii) assess how future policies relating to ambulance referral patterns and hospital closures may impact the relationship between emergency admissions and these outcomes.	Only substantive employees of IFS will have access to the data and only for the purposes described in this document. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide. All processing of ONS data will be in accordance with standard ONS Terms and Conditions. All projects are underwritten by the ESRC Centre for Public Policy at the IFS. In addition, there are some additional funding streams. 1,2 and 4 are covered by ESRC through the principle researchers ‘Future Leaders’ grant 3 and 13 are funded by an ESRC funded grant on Health Network 7 is funded by the Nuffield Foundation IFS confirm that none of the funders exert any influence over the projects and outputs. No data (other than aggregated data and small numbers suppressed) will flow outside the UK. This is particularly in relation to projects 3 and 13, but also applies to all the other projects described The IFS will not attempt, nor have a requirement to re-identify individuals in the data supplied by NHS Digital Processing activities for each project: (1) Part 1 will model changes in the probability that a woman gives birth in her nearest maternity hospital) over the past decade. IFS will assess whether women with certain characteristics (older, or from different types of areas) are more likely to bypass their nearest hospital, or whether patients travel further for maternity care at a teaching hospital. Part 2 will focus on patients who have at least two HES maternity records, and examine whether care offered during the birth of the first child affects the mother’s choice of hospital for subsequent births. There are a range of factors that could affect both the ease of childbirth and where a mother decides to give birth to subsequent children that are unrelated to the care she received, for example, her age. IFS will therefore isolate random variations in treatment, which could be used to identify the impact of health care provision. Possible examples include whether the mother gives birth at a weekend or public holiday, the number of other babies born at the same hospital on the same day. The data will be used to create a sample of women who gave birth in NHS hospitals. The pseudonymised ID will be used to identify whether, where and when these women give birth again. The data will then be used to estimate statistical models to assess which factors around the time of the first birth affected the subsequent patterns that are identified. All work is conducted using the statistical software package Stata. The HES data may be linked to aggregated geographical data relating patient or hospital. Examples include the number of women of childbearing or number of hospitals within the local area. These data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model. An example of a publicly available geographical data to be linked would be ONS population statistics (http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-315018). HES data may be linked to aggregated data at the level of the hospital (for example, whether the hospital has an alongside maternity unit) or the geographical area of the patient/hospital (For example the number of hospitals within the local area). Again, these data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model. IFS would like add extra characteristics of the hospitals, as these characteristics might affect the hospital choice of mothers. (2) Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences). As it is expected that clinical need will be a crucial input into individual patient decision making, the HES and PROMS datasets will be linked. This linkage will enable the production of a model that takes patient need into account and therefore provide more accurate analysis and predictions. PROMs data are also required over this period in order to understand how the clinical benefits following joint replacement surgery has changed over time, particularly in light of the independent sector reforms. This is essential for an analysis which estimates the implications for population health following the huge increases in the volume of joint replacements observed in the ten years prior (i.e. IFS can examine whether the clinical benefit for patients has increased or decreased as a result of greater availability of hip and knee replacements. This will help to analyse whether the independent sector reforms have increased the welfare of NHS patients). (3) IFS will calculate the percentage of patients receiving a PCI within 1 day and within 30 days of a first admission for an AMI, and compare these rates to those in the United States. IFS will also calculate these rates by region and PCT/CCG. These data will not contain rates based on fewer than 50 patients to ensure that the data are not disclosive. It will be necessary to calculate rates back to 2000 to understand whether the countries have been converging or diverging overtime as the treatment became more widespread in the early 2000s. IFS require the most recent data as the preferred journal requires data from the past 5 years to be included. As England had a large expansion in 24/7 PCI centres after 2008 following The National Infarct Angioplasty Project (NIAP), IFS have collected the months and years that each centre opened. IFS will document the resulting changes in PCI rates, and use these changes to understand how PCI and the NIAP proposals have affected patient outcomes, such as 30 day in hospital mortality and readmissions for subsequent AMI. This analysis will allow IFS to understand both the effectiveness of the NIAP proposals in England, and to understand the extent to which this roll-out explains the falling AMI mortality differences between the United States and England. IFS will not publish any statistics using HES that link to particular hospitals, or samples of patients fewer than 20. (4) IFS will model the relationship between the total number of NHS funded elective hip replacements in a middle super output area in a given year, and the introduction of Independent Sector Providers. IFS will compare the same area over time, and compare across areas by distance to the nearest independent sector provider offering hip replacements, relative to the nearest Acute Trust providing hip replacements. Data will be combined with publicly available area level and GP practice characteristics, in order to examine variation in outcomes or behaviour, or to control for potentially confounding factors at the area level. Publicly available area level data includes measures of population size and levels of deprivation. These variables are used in area-level regressions as control variables. This includes population data (available from the Office for National Statistics and local deprivation scores made available for public use by the Department for Communities and Local Government). IFS require data back until at least 2000, as the policy to increase and formalise the role of the Independent Sector began in 2003, and the ability to study a comparison group is essential to accurately identify the impact of the policy. (5) IFS will examine the age profile of English hospital spending across the period between 1997/98 and 2013/14. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for all inpatient activities to different age groups. Using publically available data on the English population, the average spending per individuals of a given age can be derived. IFS will then examine how this develops over time, providing evidence on whether average spending for individuals of a given age has changed over time (i.e. is the average spend for a 70 year old male in 1997/98 different to a 70 year old male in 2013/14). The pseudonymised HES indicator will be used to track the use of hospital care for a random sample of individuals in each year of the data. This will allow an estimate of total healthcare expenditure for individuals over the entire period, providing a measure of “lifetime medical spending” for older individuals. It also allows IFS to examine the correlation between health spending in one year and another (i.e. does health spending in one year predict health spending in the next, or five years later etc). Individuals who die in hospital are recorded in HES (through the discharge method variable). For individuals who die in hospital, IFS can examine the amount (and cost) of hospital care received in the final year(s) of life. In this way, IFS can estimate the amount and the share of hospital expenditures that are incurred in the final year of life. These estimates can then be compared to the findings of other researchers who are conducting a comparable analysis on similar data in other countries such as the USA and other European countries (Note: IFS will not combine the data with these other researchers, but only examine regression coefficients and the findings of this research). IFS require data back to 1997/98 to provide the longest time series possible over which you can track individuals using the HES identifier. This will (i) provide the largest history for individuals (and therefore acts as the best proxy for lifetime use of the service) and (ii) provides a significant period of time over which to examine how the distribution of spending across ages has developed (i.e. IFS can examine whether the average 70 year old in 2013/14 uses more healthcare than a 70 year old in 1997/98). (6) IFS will model the impact of rapid immigration on the demand for accident and emergency services by comparing the change in (i) inpatient admittances for ambulatory care sensitive (ACS) conditions and (ii) visits to A+E, across local authorities with different changes in the concentration of foreign born residents (population data at the local authority level is sourced from the publicly available UK Labour Force Survey). Admittances for ACS conditions are derived from OPCS-4 codes. A similar exercise will be conducted with admittances for maternity patients, comparing the number of birth episodes recorded by inpatient HES across these regions. IFS will then also examine the number of 30 day readmissions for newborn children across these areas, using the pseudonymised HES identifier, to examine whether the quality of maternity care has deteriorated in an observable way in areas where the population has rapidly grown. This will provide evidence on whether NHS trusts adapts quickly to changes in the size and the characteristics of the population which they treat. HES A&E data is required for the most recent period of time in order to understand the use of the service during a period which has witnessed significant changes in the size, and composition, of the English population. This provides sufficient variation in the data to attempt to estimate causal impacts of population change on demand for, and quality of, A&E services. (7) The same personnel who currently process data for projects involving all HES records will create a dataset that contains only admissions for individuals under the age of 30. The dataset will be placed in a separate secure area for the project team to use, so that they are able to access only the data needed for the project. To investigate the relationship between Sure Start and hospital admissions, IFS will merge information on the location of Sure Start Centres into HES using LSOA identifiers. IFS will then test whether cohorts exposed to Sure Start (both overall and accounting for intensity of exposure) are less likely to experience hospitalisations and outpatient visits (all-cause and cause-specific) and A&E admissions (from 2007-08 onwards). The two sets of treatment and control groups will be compared: those who lived at ages 0-4 (i) in areas that implemented Sure Start earlier vs. later and (ii) in areas that experienced larger vs. smaller expansions of Sure Start. To understand the roll out of Sure Start, IFS will examine the determinants of the timing (the year of opening of the first Sure Start Centre in a given Local Authority (LA)) and the intensity (the number of Sure Start Centres in a given LA per year) of the rollout. This will include pre-programme levels and trends in hospitalisations and outpatient visits (all-causes and cause-specific) among the determinants. (8) The same team who currently process data for projects involving all HES records will create a dataset that contains only drug related admissions. Episode level data on hospital admissions will be used to compute the frequency of drug-related hospital admissions. This will focus on cannabis related hospital admissions as well as other drug-related and alcohol-related admissions, and will be computed by time period (monthly and yearly) and region (especially TV region equivalents, the regional level at which the tobacco sales data are available) , and in relation to demographic characteristics (such as age group and gender). The output of the analysis will be aggregate data (by region, time period, and for demographic groups), with small numbers suppressed in line with HES analysis guide. This will then be compared with the incidence of cannabis-related hospital admissions to estimates of cannabis market size, which are constructed from other data sources (especially sales data for tobacco-related products). Overall, this will allow IFS to compare market-size estimates to admission based estimates of heavy drug consumption for cannabis, as well as other drugs and alcohol, and to test the relationship between these variables across regions and over time. As the sample sizes are likely to be small, IFS will ensure that only aggregate numbers are reported and any small numbers are suppressed. (9) IFS will use episode level data to compare 30-day in-hospital mortality rates of patients treated by different consultants following admission to an NHS hospital for an AMI or stroke. Admittances for AMI and stroke are derived from ICD-10 diagnosis codes contained in HES. Consultants are assigned to patients in HES using the anonymised consultant ID (variable ‘pconsult’). Patients who die in hospital are recorded in inpatient HES (through the discharge method variable). Anonymised patient IDs will be used to examine whether patients who are discharged but then readmitted during the 30 day period following the initial admission die in a subsequent hospital spell. The analysis requires the construction of detailed control variables to account for differences in the underlying health of patients treated by different consultants and hospitals. Failing to account for these differences will lead to inaccurate estimates of the effects that each consultant has on patient outcomes. Detailed measures of health conditions and past hospital use are therefore essential for this analysis. IFS will derive a range of clinical indicators using the ICD-10 diagnosis codes in HES, and use these to create the Charlson Index to capture patient morbidity. Using data from 1997/98 – 2014/15, IFS will use the anonymised patient identifier to track patient inpatient admissions and outpatient attendances over time in order to construct detailed histories of patient hospital use. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for each of these activities to summarise past hospital use. IFS will also create a variable for each year which indicates whether a patient has been treated for a heart attack or stroke in a previous year. Previous research has shown that a major determinant in survival following a heart attack is the amount of time that elapses between onset and treatment, and the distance that patients need to travel to reach a hospital for treatment. This will be addressed by examining the distance between the Lower Super Output Area of patient residence and the hospital in which they are treatment. In addition, for the period 2007/08 – 2014/15, IFS will use the Accident and Emergency data to examine whether the onset occurred at home (variable ‘aeincloctype’) and the time that elapsed between arrival at hospital and admission (variable ‘tretdur’). In order to separately estimate the impact of consultants from the hospitals in which they work, the analysis needs to control for differences in the types of patients treated by different hospitals. It also requires the observation of consultants working in different hospitals over time. IFS will address the first point by combining publicly available aggregated geographical data relating to the socio-economic status and population health to summarise the characteristics of the patient population served by each hospital. Inpatient HES data will also be used to create other indicators of patient health and quality of local primary care (e.g. the admission rates for ACS conditions in the local area), and the quality of other care provided in the hospital (e.g. hospital level readmission rates for elective hip replacements). In addition, for the period 2007/08 – 2014/15, Accident and Emergency data can be used to separately analyse the outcomes for patients who arrived at the hospital in an ambulance (contained in variable ‘aearrivalmode’). This would allow analysis on a subset of patients for which it is certain that patient did not choose the hospital in which they were treated, and therefore rules out matching of (otherwise unobservably sicker) patients to hospitals which could potentially bias results. The second point is addressed by following consultants across hospitals over time, through the use of anonymised consultant team variable (‘pconsult’). This allows a comparison of patient outcomes when treated by the same consultant but in a different setting. IFS require inpatient and outpatient data back to 1997/98 for two reasons. First, the analysis relies on consultants moving across hospitals over time. Using the longest available period of data will capture substantially more movement in staff across hospitals, and will maximise the sample of patients whose outcomes can be studied. This will increase the precision of the estimates. Second, the panel element of the data will be used to construct detailed histories of hospital use for patients. This will improve the accuracy of the analysis by controlling for a broad range of factors in the underlying health of patients. IFS requires A&E data back to 2007/08 to supplement the analysis of inpatient and outpatient data. Using the full period of available data will maximise the number of patients for which full information on care received from arrival at hospital to discharge is available. Information on the use of an ambulance will provide a subset of patients who IFS can reasonably assume have no choice over the hospital they attend. IFS will the conduct the analysis for the period between 2007/08 and 2014/15 both with and without use of the additional information of the method of transport to examine whether patients who do not use ambulances selectively sort into particular hospitals. The analysis can then be extended to earlier years (prior to A&E data availability) with a better understanding of whether patient sorting between hospitals occurs. The analysis on the role of independent sector providers within the NHS requires inpatient HES data up to 2013-14. When examining the impact of these reforms, it is essential to understand whether the trends seen between 2000-1 and 2010-11 continue between 2010-11 and 2013-14. This is a period of time in which (i) NHS funding was relatively restrained and (ii) the wider economy was recovering from a large recession in the preceding years. As a result, to evaluate the impact of the reform on patient health and the quality of NHS services provided, it is crucial to examine the longer term impacts of the reforms. For the work on heart attack treatment, IFS intend to submit this work to the Journal of the American Medical Association, which has a strong preference for work that covers the past 5 years. Importantly, in all cases the ability to conduct research on the most recent years of data is crucial for the analysis to be both timely and relevant. This maximises the impact of this research on feeding into current policy debates such as the extent to which private providers are used within the NHS, and how the NHS has responded to the challenges posed by a growing and ageing population. All outputs from each project will contain only aggregate outputs with small numbers suppressed. IFS report aggregate summary statistics (i.e. total number of women giving birth in NHS hospitals in 2010/11) and regression coefficients from large-sample regressions. No record level data will be shared with third parties. Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences). From May2017 (10) IFS will use the A&E HES data from 2010/11 - 2015/16 (at record level) only to study whether the probability of inpatient admission changes for patients who are admitted during a period close to the four-hour waiting limit. This will be achieved by computing the counterfactual probability of admission in the absence of the target. IFS will calculate this counterfactual by estimating a polynomial regression (regressing admission on the period of admission) for all patients who are feasibly not affected by the target (e.g. all patients admitted/discharged from A&E between 0 – 180 minutes, and after 240 minutes). IFS will use data on patient characteristics and investigations/symptoms contained in the A&E data, along with diagnosis codes for admitted patients (in their APC HES records) to examine how the characteristics of patients vary with time. APC HES records will be used to examine how treatment intensity varies across patients who are admitted at different points of time. For example, IFS will examine whether length of stay for inpatients admitted after a shorter period of time in the A&E department are different from those who are admitted after waiting for longer. IFS will also examine how the probability of readmission to hospital varies across these individuals. IFS will use the linked HES-ONS mortality data (at patient record level) to examine whether the probability of death varies across individuals who are admitted or discharged after different waiting times. In-hospital mortality could be recovered from unlinked HES. However, out-of-hospital mortality may also change as a result of the policy. The inclusion of the ONS mortality data would therefore allow all-location mortality outcomes to be examined. IFS will also examine the underlying cause of death, to investigate whether cause of death is the same as the major diagnoses when patients recently visited hospital. (11) IFS will construct trust level measures of quality of care for different services at annual level between 2000 and 2014. These measures of service quality will be related to changes in admissions for these services. IFS will examine changes over a long period to allow them to follow dynamic effects of the policy shift towards competition and choice. IFS will focus on specific services which have been examined previously in international and national studies, and include treatment for the following conditions: Acute Myocardial Infarction (AMI), heart failure, pneumonia, and hip and knee replacements. For each condition, IFS will create site and trust level measures of outcome quality (risk adjusted 30-day survival rates and risk-adjusted 30-day readmission rates), and condition-specific measures of process of care which capture the hospital’s conformance with established clinical guidelines for care (e.g. for AMI this includes the administering of aspirin, ACE inhibitors, smoking cessation advice, beta blockers, and angioplasty). Patient data at the record-level will be required to (a) construct risk adjustors and (b) to allow separate analysis of the data by local-area deprivation status (measured by IMD), in order to examine whether patients of lower Socio-economic status (SES) gained less or were harmed by these policies. This is an important component of the analysis and hence the need for HES record level data. IFS will also match nationally published data on quality and processes of care from other sources at the trust level. These sources include data from MINAP (for treatment of AMI and heart attack patients); the health care regulator; published trust level PROMS data (for joint replacements); staff satisfaction (from the annual staff satisfaction survey) and patient satisfaction. These data will not allow IFS to examine within trust across patient SES type differences as they are aggregate data at trust level. (12) Data on UNICEF Baby Friendly hospitals (including the date in which the status was acquired) will be obtained from UNICEF. The data will be merged with individual-level records for babies from inpatient HES, using the site code (procode5) to identify hospitals. IFS will estimate multivariate regressions of health outcomes and care use (re-admission, A&E visits, presentation of certain conditions) to examine whether hospitals that are compliant with the UNICEF Baby Friendly Initiative deliver better patient outcomes. The use of hospital fixed effects will allow IFS to control for endogenous placement of the UNICEF initiative (e.g. better or worse hospitals are systemically enrolled in the initiative). Publicly available information on the socio-economic status of the population at the local area level (e.g. the ONS created Index of Multiple Deprivation at the Middle Layer Super Output Area) will be merged with the hospital data to capture differences in patient characteristics across areas. HES records will also be merged with data IFS have collected on the community service providers that comply with the UNICEF Baby Friendly Initiative, using the postcode of the community service and the postcode of the General Practitioner of the patient. IFS will then conduct the same type of analysis previously outlined at the hospital level for community services. (13) IFS will use publicly available bibliographic sources (e.g. PubMed, Web of Science, academic journal websites) to calculate the number of publications authored each year by staff employed by each NHS trust. This will be merged with hospital-level inpatient and outpatient HES data and A&E records to identify key medical treatment innovations in the period between 1997 and 2015. IFS will use this to estimate whether the health outcomes (e.g. mortality, readmission, A&E visits) of patients who are treated for conditions associated with these medical innovations improved more rapidly over time in research-intensive hospitals (as measured by the number of topic-specific publications) (14) IFS will calculate the 30-day hospital readmission and in hospital mortality rate for children born on each day of the week, using inpatient HES data from September 2000 to August 2001. IFS will then uses these figures to compare readmission and in hospital mortality rates for children born Friday - Sunday, to children born Monday - Thursday. Only aggregate figures will be published (by day of the week), ensuring that large sample sizes are used in all cases. This will be used to supplement existing work on this project which makes use of the Millennium Cohort Study, 2005 Infant Feeding Survey, and 2007 Maternity Users Survey (note, IFS will not link individual level HES records to individual records in any of these survey (Millennium Cohort Study, 2005 Infant Feeding Survey , 2007 Maternity Users Survey). (15) The project will use inpatient data for the years 1997 to 2010 and A&E data for the years 2007 to 2010. Data will be extracted for trauma and orthopaedic departments, identified using consultant specialty information. Sub-objectives (i) and (ii) will involve building statistical regression models that compare the number of emergency admissions at each hospital with patient outcomes at that hospital. Sub-objective (ii) will assess how this statistical relationship has changed over different time periods and hospitals, for example before and after the introduction of Payment by Results in 2004. Sub-objective (iii) will involve linking the inpatient and A&E data to establish which patients at trauma and orthopaedic departments arrived by ambulance. Statistical models will then be used to evaluate how the number of emergency admissions, and the associated impact on patient outcomes, would change if ambulances were to assign patients to hospitals differently or if certain A&E departments were to close. This application covers data from 1997/98 to 2016/16. It is important to have data that covers this period for three key reasons. First, a number of the research aims are to investigate the impact associated with different policy changes that have taken place during this period. In each case, IFS need data from before and after the policy change. For example, project (4) studies the impact of introducing private providers into the NHS market for elective care, which took place as part of a set of reforms throughout the 2000s. Having data from before, during and after this period is essential in understanding the changes that took place as a result of these reforms. Second, data from 1997/98 to 2016/17 will provide the longest time series possible. This will allows IFS to better understand trends in NHS activity over time (e.g. in project (5) this allows IFS to examine how NHS activity has developed across birth cohorts, and in project (11) to examine whether the impact of competition has changed over this period. Finally, using data from multiple years helps to maximise sample size. This is crucial in boosting the statistical power of the research, helping to accurately identify and estimate effects. As a result, these data requirements are essential in allowing IFS to carry out the proposed research.	(1) There will be three written outputs: (i) an IFS working paper produced in the next year, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care. Staffing constraints have meant that progress was slower than anticipated. However, the work has been discussed with the Royal College of Midwives and the North East London Sustainability and Transformation Planning Team. The results will be presented at a one-day event in September aimed at policy-makers. Written outputs are expected to follow in late 2017 and 2018. (2) The outputs for this project are similar to those for project (1). The aims are to produce written outputs that are widely cited in the academic literature and encourage more academic work on the NHS, and non-technical summaries that will provide information for policy makers that would otherwise be unavailable or very costly for the Department of Health or Monitor to acquire. As this project is more complex, IFS expect outputs in the next 1-3 years. The model IFS produce will have the capacity to model potential policy scenarios. IFS will interact with the Department of Health to ascertain whether there are any policy scenarios they would like IFS to model to maximize the value to the Health and Social Care system. IFS has presented the work at the Royal Economic Society and European Economic Association conferences, and discussed finding with the Competition and Markets Authority. A working paper is complete. This will be published in June 2017, when the paper will be submitted to a journal. (3) There will be two sets of outputs from this work. It was intended that the first set of work would relate to PCI use in England and the US, comparing same day and delayed PCI rates, and volumes per facility. However, this work has been on hold due to data delays in the US. The second set of work will examine the impact of the roll out of PCI centres in England on patient outcomes. Outputs will include submission to a economics journal, such as the Journal of Health Economics, and an IFS briefing note and press release. These are still on track to be delivered in the second half of 2017. Given the direct policy-relevance of this analysis, IFS will contact Department of Health, NHS England, and NICE in order to present and discuss the findings, to ensure that they are aware of the results and to check the validity of any assumptions that have been made. (4) Some of these outputs from this project, including several conference presentations, a policy presentation and a non-technical policy summary, have already been produced under the previous license agreement. Previous conference presentations included a workshop attended by representatives from the Department of Health, Monitor, the Nuffield Trust, the Office for Health Economics, and the Kings Fund, and economics academic conferences including the Royal Economic Society conference. IFS submitted an article to the Journal of Public Economics, the leading academic publication on public economics (including health economics) in early 2017. IFS are currently waiting for a decision from the editor. (5) The principal outputs are (i) a working paper, which was published under the IFS working paper series (see project 1) in August 2015 (https://www.ifs.org.uk/uploads/publications/wps/WP201521.pdf), (ii) an academic conference presentation in March 2015, (iii) a peer-reviewed journal article in the economics journal Fiscal Studies, which was published as part of a special issue of Fiscal Studies on cross-country comparisons of health spending across the lifecycle in November 2016 (http://onlinelibrary.wiley.com/doi/10.1111/j.1475-5890.2016.12101/full), and a non-technical, policy summary (https://www.ifs.org.uk/publications/8737). IFS will present the results in dissemination events with policymakers, funded by the Economic Social and Research Council in September 2017. Fiscal Studies is a peer-reviewed economics general with all articles explicitly aimed at bridging the gap between academic research and policy, with a reputation for publishing timely high-quality articles that are easily accessible to policymakers. A workshop to discuss preliminary findings took place in March 2015. This workshop was attended by representatives from the Department of Health, who subsequently invited IFS to present the findings at the Department. IFS have spoken to the OECD about this work, who believes it could help inform their highly influential work on cross-country comparisons of health systems. The ESRC event will be exclusively focused on policymakers, with invites to representatives of DH, Monitor and PCTs, along with members of influential health policy research groups such as the Kings Fund and Nuffield Trust. IFS expect outputs from this project in 2017. (6) The principal outputs will be (i) academic conference presentation at the European Economists Association (EEA) Annual Conference in August 2015, the Royal Economic Society (RES) Annual Conference in March 2016, the Intertnational Institute for Public Finance (IIPF) in August 2016, and the European Association of Labour Economists (EALE) Annual Conference in September 2016, (ii) a working paper, published under the IFS working paper series, , and (iii) the submission of a peer-reviewed journal article to the Economic Journal. The benefits of these outputs are discussed above. Output (i) has already been realised. The EEA, RES, IIPF and EALE Conferences are attended by the leading economists in Europe and the USA, and in light of comments received at the conferences, the work on this project has been extended. As a result, the original target date for the publication of the working paper and journal submission was pushed back from Spring 2016 to Summer 2017. IFS have produced a non-technical policy summary for policymakers. This has been sent to the Health Foundation for comments, and will be sent to representatives of Department of Health, Monitor and NHS England in the next few months. (7) In the first year of the project, the main outputs will be an interim report submitted to the funder: due in July 2016 on the impact analysis of the determinants of the rollout of Sure Start. The initial project planned specified that in the second year (2017), IFS would have three main outputs: (1) the final report, to be submitted to the Nuffield Foundation in February 2017; (2) an IFS working paper (see above), and (3) a related academic paper. The timeline of this project has been pushed back a year after delays in receiving essential non-HES data for the wider project (an application for local area level Health Survey for England data was submitted to NatCen and the Health and Social Care Information Centre in 2015, and is ongoing). Outputs (1), (2) and (3) are now expected in 2018. Both report and paper will be available on the IFS website. The academic paper will be targeted to a top economic journal, such as the Economic Journal (see above). The findings of the report will be disseminated by press release and an IFS policy observation (on the IFS website) in order to reach target audiences in the media and general public. A launch event will be organised at IFS, where the results will be presented and academics will be invited (experts in early years policy) and policy makers (MPs working on early years policy of the All Party Parliamentary Group) to discuss their implications. Finally, IFS will present the findings at major economics conferences (such as the Royal Economic Society, RES), to gain comments from other academics working in the same field before submission to a top-tier peer-reviewed economic journal. (8) IFS expect written output within the next 1-3 years. This will take the following form: an academic economics journal article submitted to the American Economic Journal: Economic Policy (AEJ-EP) or the Economic Journal (EJ). Both of these journals are leading international peer-reviewed Economics journals. The principal audience is economics academics who will read and cite the paper. Other outputs will include presentations at academic conferences and seminar presentations, which will focus on receiving comments from economists on how to improve the analysis. (9) The project is expected to produce a range of outputs, including ((i) multiple academic conference presentations to general economics (e.g. the Annual Royal Economics Society conference) and health economics audiences (e.g. the annual meeting of the UK Health Economists Study Group); (ii) an IFS working paper (see above); (iii) the submission of a journal article to a leading peer-review economics journal, such as the Review of Economic Studies (Impact Factor: 4.038) or the Journal of Health Economics (IF: 2.579); (iv) a non-technical policy summary, which will be press released and sent to contacts at the Department of Health and NHS England. The Health Economists Study Group is a work-in-progress conference attended by the leading health economists in England, and representatives from NHS England, the Department of Health and leading health policy organisations such as the Health Foundation and the Kings Fund. Their comments will give the researchers the chance to improve the analysis and focus the findings in the most informative way for policy. Output (i) has been realised in 2016 and 2017, with a number of presentations at leading UK universities (UCL, King’s College London, Oxford) and a conference presentation at the International Institute for Public Finance (IIPF) Annual Conference planned in August 2017. Outputs (ii) – (iv) have been delayed while in discussion with NHS Digital about access to workforce data. IFS aim to produce these outputs in 2018. In the first year, the main outputs will be presentations and discussions with the Department of Health and NHS England to check the validity of the assumptions underlying the model that IFS estimate, and to identify where the model could provide information and simulations that are useful to policy-makers. As the estimation of the model is reasonably complex, written outputs are expected over the next 1-3 years. These will take three forms: (i) an IFS working paper, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care. From May 2017 (10) The analysis will be used to produce a range of outputs.Three types of written output are expected: (i) a working paper, published as part of the IFS working paper series, (ii) a peer-reviewed journal article submitted to a leading peer-reviewed economics journal; (iii) a non-technical research summary which will be press-released, and shared directly with NHS Improvement and the Department of Health. This work has already been discussed with economists at NHS Improvement who are also working on projects to examine the quality of care in A&E departments. Other outputs will include presentations at academic conferences and to government departments. IFS would aim to produce a working paper and a policy report by December 2017. Articles in economics journals typically take 2-3 years from the start of the work to publication in a peer-reviewed journal. IFS would therefore expect an article to be published in 2018 or 2019. (11) The expected outputs are similar to (1), with the publication of a working paper, submission to a peer-reviewed health/economics journal, and a non-technical summary aimed at policymakers. (12) There will be three written outputs: (i) an IFS working paper produced in the next year, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to an internationally recognized peer reviewed economics journal; (iii) a non-technical research summary which will receive a press release and target policy makers from UNICEF, Department of Health, Monitor, NHS England and the CQC. Other outputs will include presentations at academic conferences and presentations to policy makers involved in the planning and delivery of NHS care. (13) There will be three types of written outputs: (i) a working paper, (ii) a submission to a peer-reviewed economics journal and (iii) a non-technical report to disseminate findings to interested policymakers and funders of national research, particularly the Medical Research Council and the Economic and Social Research Council. Outputs (i) and (iii) will be produced in 2017. Publication in a peer-reviewed journal will occur in 2018 or 2019 (due to long publishing times). IFS will also present the results to the ESRC-National Institute of Health (US) funded network on the value of Medical Research. This network meets twice a year in either the UK or the US and includes economists and medical professionals interested in the field of medical innovation. (14) There will be three written outputs: an IFS working paper (this will update a previous version that did not include HES data), (ii) a resubmission to a leading economics journal (a previous version, without HES data, was submitted in September 2015. IFS have been invited to resubmit a new version), and (iii) a non-technical report discussing the results of the work in order to benefit policymakers, and other entities interested in breastfeeding, such as UNICEF and the National Childbirth Trust. (15) This research project, which was previously under another data agreement, has already produced a number of outputs. This has included presentations at the Royal Economic Society, University of Manchester, University College London and Northwestern University, as well as a series of discussions with NHS Improvement and a range of NHS employees (managers, physiotherapists and nurses). These existing outputs relate to sub-objective (i), and the work will now be extended to sub-objectives (ii) and (iii). Planned outputs include a draft research paper for circulation in September 2017 and further presentations that are currently being scheduled between May 2017 and January 2018 (at various conferences, universities and at NHS Improvement). Following this the research will be published as a working paper (available via the IFS website) and submitted for publication to an academic economics journal in late-2018. All outputs will be in aggregate and comply with the HES Analysis guide for small number suppression.	The existing work using HES data has been used to inform policy maker Monitor, NHS England, the Department of Health, the Cabinet Office and representatives from PCTs. As noted by the Department of Health, academic work helps to understand the impacts of former policy and how to improve the existing health and social care system. Examples of previous outputs, and the steps IFS have taken to disseminate the findings to policymakers, include: •The research report “Choosing the place of care” was published in 2012. A summary presentation has since had 9,505 views online. After the report was published, representatives from Monitor, the Department of Health, and NHS England met to discuss the policy implications of these findings. In the months that followed, economists from the cabinet later requested a meeting to ask for advice on NHS competition. •The research report “Public pay and private provision” was published in 2013. The accompanying presentation has now been viewed 16,342 times online. The results were presented at the Nuffield Trust’s Competition for Care conference in May 2013, which also included speakers from the Competition and Cooperation Panel, Monitor, NHS England and the NHS Confederation. The audience included both national policy makers and local commissioners. The results have also been presented to a meeting of Conservative Health at the House of Commons in July 2013. •‘Policing Cannabis and drug related hospital admissions: Evidence from administrative records’, an article in the Journal of Public Economics (released April 2014). The Journal of Public Economics is a highly respected peer-reviewed economic journal. The article has 7 citations thus far. • IFS have invited representatives from the Department of Health, NHS England, and Monitor to two workshops on academic findings on health care in 2013 and 2015, which they have chosen to attend. This indicates that academic work in general, and this work specifically, is valuable to them. Following the 2015 conference, IFS were invited to present the results to the Department of Health, further indicating the importance of this research. Following discussions with DH delegates, IFS are planning to modify their research (distinguishing between spending on elective and emergency care), as it was suggested that this could be useful to the Department during the upcoming spending review. Future projects will have a range of direct benefits to health and social care over the coming years: (1) Results from this project will improve the understanding of how and why patients make choices, and more specifically, how women respond to the quality of maternity care. This will benefit the health and social care system in two ways. First, it will assist policy makers in deciding how effective patient choice is in promoting competition between hospitals and therefore driving up standards. Second, it will inform Acute Trusts about how women make choices about where to give birth, and the potential financial consequences for Trusts who lose patients as a result of providing poor quality care. The interim findings of the project (along with those relating to project 6) were shared and discussed with representatives from the Royal College of Midwives in July 2016, and the North East London Sustainability and Transformation Planning Team in December 2016, and will inform NELST going forward on staffing decisions in the fact of increased patient demand. These benefits will be realised over the next 3 years (2) The model of hospital choice can be used to model how patients respond to potential policies, such as the reorganization of hospital services, or hospital mergers. The focus on equity is in line with NHS England principles of promoting equality and equity in provision, and the objectives of the Department of Health (see attached letter). IFS will liaise with the Department of Health to understand how the model could be more useful to them. Again, these benefits will accrue over the next three years. (3) The government has previously used cross-country comparisons to assess the effectiveness of the health care system. The results will aid such comparisons. The results will also provide information that can be used to evaluate the implementation of NICE guidance with respect to the treatment of heart attack patients between 2008 and 2010, which recommended the use of Percutaneous Coronary Intervention for certain patients. (4) Policies of the previous two parliaments have increased the role of the private sector in delivering NHS funded care, yet there is very little evidence on the impact of these providers on the impact for NHS funded care. This research will help inform policy makers of the potential effects on patient demand, health care supply, the financial implications for NHS providers, and the equity of provision if the role of these providers is expanded in the future. These issues are of importance to the Department of Health, Monitor, the Cabinet Office and NHS England. Thus far, this has been demonstrated by both the Cabinet Office and NHS England requesting updates to this work, indicating that this work has the potential to feed into policy-making in the short to medium term. A working paper version of this research was warmly received by a number of representatives from Monitor, including the Economics Director, Cooperation and Competition at Monitor, when presented in September 2013. (5) Understanding how population health care needs are likely to change is important for both national policy makers and local commissioners, particularly given tighter NHS budgets. The Department of Health has already shown interest in this work, part of which IFS have been invited to present to the Department in June. The aim is that the findings will also feed into initiatives such as the Better Care Fund and help to achieve efficiency savings set out by the QIPP initiative and NHS England’s Five Year Forward View. This research is supported by the Department of Health, who acknowledged its vital importance and provided supporting evidence for IFS funding applications to the Health Foundation. Representatives from the NHS England Strategy Group have also indicated to IFS the importance of such work in informing future policy when meeting to discuss the work. (6) Changes in the volume and composition of the population have important implications for the quantity and type of health services demanded by patients. This work will show how rapid population change can affect demand for services, and therefore help CCGs and Acute Trusts plan for the future. This could be in terms of how to organise primary and secondary care services or how to determine future staffing levels. The focus on A&E services should be particularly useful, given the difficulties experienced by A&E departments this past winter. These benefits will accrue over the next 3 years. IFS have already discussed findings with the North East London Sustainability and Transformation Planning Team. These discussions (also relating to project 1) helped NELST to internally review their provision of maternity care (underway in December 2016) following large increases in the number of mothers seeking care at their hospitals. (7) The analysis of Sure Start will provide a detailed and thorough cost-benefit analysis of the programme. Sure Start was funded at £1.8 billion in 2010-11 and accounted for a third of government expenditure on early years programmes. Funding per eligible child fluctuated over time but averaged about £6,500 per year. This reflects a considerable government investment in early years interventions, and it is important to understand whether this intervention provided value for money by improving subsequent outcomes. One of the major rationales for Sure Start is the evidence from other countries that early intervention is more cost-effective than treatment for poor child health. There has been a renewed focus on early intervention in the UK; for example, a 2012 report by the Chief Medical Officer (CMO) called urgently for more investment in early years to prevent poor outcomes later in life. Similarly, the NHS Five-Year Forward View (2014) called for a ‘radical upgrade in prevention.’ The government has made explicit plans to deliver part of this early intervention through Sure Start; for example, the 2009 child health strategy ‘Healthy lives, brighter futures’ envisages a strengthened role for Children's Centres in improving children's health and supporting parents from pregnancy onwards. Poor child health generates substantial costs for children, their families and the Health and Social Care System . Research by Action for Children and the New Economics Foundation suggests that, over a 20-year period, preventable health and social outcomes faced by children and young people will cost £4 trillion (‘Backing the Future’, 2009). Even the short-term costs imposed are considerable; for example, the short-term hospital costs of severe unintentional injuries to children are estimated at up to £87 million per year (CMO’s Annual Report 2012, Chapter 3, p. 8). Potential long-run costs could be in excess of £2 billion (ibid). Using HES to understand whether Sure Start is an effective way to reduce the rate of these hospitalisations is a concrete example of how IFS hope to add value to the health and social care system. Understanding the role of community based health services is vital in designing new models of care that both deliver better value to the patient and their families, and help the NHS to continue to deliver high quality care with constrained resources. Given the potential importance of this work to the health and social care system, IFS are strongly committed to reaching out to policymakers to disseminate the findings. To do so, IFS will produce written work targeted at policymakers, including a non-technical report describing their methodology and their findings. This will be freely available on the Institute for Fiscal Studies website. IFS will also provide support in communicating the results, including through a press release; a brief observation note highlighting key findings; and a launch event which will be open to policymakers and the media. This will take place in early 2017 (the end of the project and release of the report). In addition, IFS plan to reach out directly to key policymakers within the health and social care system. IFS will approach strategists at NHS England and Monitor to discuss the results of their cost-benefit analysis of early intervention. This will give them rigorously-researched information on the effectiveness of early interventions, which they can use to inform and support future strategies. IFS also plan to communicate with health policy organisations, such as the King’s Fund. In addition, this work will have wider impacts beyond the health and social care system. IFS are already working closely with policymakers within the Department for Education as well as practitioners from the early childhood development field. IFS have the strong support of this advisory group in maximising their policy impact, including their assistance in disseminating the results widely through their networks. Finally, IFS have existing links with politicians from the all-party parliamentary group 1001 Critical Days and the Foundation Years Information and Research group. These groups have already expressed interest in the results. Once the report is published in March 2017, IFS will meet with them to discuss the results and the policy implications of their findings. The findings will inform the recommendations of these groups and will provide a stronger evidence base for early intervention in the UK and ensure value for money is delivered. (8) Quantifying the market size for cannabis is important given vigorous policy debates about how to intervene in this market. A body of evidence across disciplines has established significant private and social costs associated with the market for cannabis. Private costs borne by users include longer-term impacts on health from prolonged and heavy use [Fergusson and Horwood 1997, Hall and Degenhardt 2009, Marshall et al. 2011], as well as a potentially increased propensity to use other illicit substances [van Ours 2003, Kelly and Rasul 2014]. The social costs of the cannabis market arising through the health systems are substantial. For example, Public Health England estimates that drug misuse costs the NHS in England £488m annually (Public Health England 2013). Better understanding of the market size of cannabis can therefore bring measurable benefits to health and social care. The statistics based on the HES allow to compare cannabis market size estimates (by TV region equivalent and time period) based on sales data from tobacco-related products to hospital admissions for related diagnoses, and will assist policy makers by providing better understanding of how these measures are related. In particular, better understanding of the market size for cannabis can inform policy makers in the area of health and social care by providing important information about the size of the cannabis market, including as input into cost-benefit analysis (CBA). NHS Health Scotland has made extensive use of alcohol sales data to monitor and evaluate Scotland’s alcohol strategy (NHS Health Scotland 2016), and concluded that sales data can contribute to ensuring that “consumption or related harm is spotted early”, and identified as an area for future research the relationship between “consumption [of alcohol] and harm within Scotland and the rest of the UK” (NHS Health Scotland 2016). The proposed project provides evidence on such a question in the context of drug use, and will assist policy-makers by providing insight into whether such an approach could be used for illicit substances such as cannabis, and develops a new method for doing so. IFS will write a policy-focused summary of the results aimed at a practitioner’s audience and the general public, using widely read dissemination websites such as “VoxEU” (http://voxeu.org/content/topics/health-economics) or “The Conversation” (http://theconversation.com/uk/health). For example, previous policy-relevant research summaries by the researchers on VoxEU have been accessed more than 20,000 times. Links with a prominent professor (University of Essex), who has worked and advised widely on topics related to health and risky behaviours, will also help to disseminate findings to a policy-maker audience. IFS will distribute the findings to their links and contacts in the NHS. The research paper will be available freely on their website. These benefits will accrue of the next three years as the paper is prepared, as well as subsequently when the results are available in the publication. ADDITIONAL CITATIONS: NHS Health Scotland (2016): “Monitoring and Evaluating Scotland’s Alcohol Strategy,” Final Annual Report, March 2016. Public Health England (2013): “Alcohol and drugs prevention, treatment and recovery: why invest?,” PHE publications gateway number: 2013-190. (9) Consultants play a crucial role in the delivery of NHS healthcare. However, little is understood about the extent to which patient outcomes depend on the individual consultant who is responsible for their care. This project will provide evidence on the distribution of consultant effects. IFS will meet with NHS England and the Department of Health to discuss the results of their analysis and how the model could be used to guide policy to benefit the health and social care system. The model be used for policy experiments such as “what is the impact of patient survival rates if the 5% of worst performing consultants were replaced by median-performance consultants?”. DH, NHS England or Acute Trusts may use this as a basis for deciding whether some consultants require more training, additional support from the Acute Trust, or should be moved to other positions. Similar work has been carried out in the past in the United States and these models have been widely adopted by State hospital boards to evaluate provider performance, with positive implications for patient outcomes. By 2006, 47 states used similar models to produce publicly available ‘report cards’ for hospitals. Economic evaluations of the adoption of these hospital performance measures have indicated substantial improvements in the quality of care received by patients at previously poorly-performing providers. For example, researchers found a reduction of a third in the mortality rate of patients undergoing coronary artery bypass graft surgery in New York State between 1991 and 1997 in hospitals which had received a ‘high-mortality’ flag in the previous year (Cutler, Huckman and Landrum, 2004). Similarly, surgeon-specific report cards in Pennsylvania led to significant improvements in risk-adjusted mortality rates in the following years (Kolstad, 2013). IFS researchers have also met with a number of policymakers to discuss a number of projects covered by this agreement. In December 2016, they met with representatives from the Competition and Markets Authority (Chief Economist) to discuss findings from projects 3,4 and 9. These discussions will inform CMA analysis relating to hospital mergers (on the consequences for technology, hospital competition and the NHS workforce). In May 2017, IFS researchers met with representatives of Public Health England (Chief Economist) to discuss work on projects 3, 4 and 5. IFS researchers also met with representatives from the Department of Health in May 2017 (Chief Economist) to discuss IFS work on health care (specifically projects 1,4,6 and 9). These discussions will help to inform future PHE and DH internal analysis on provision of NHS health care, and IFS will provide further updates to these policy makers when the results are finalised. From May2017 (10) This analysis will improve knowledge of how the four-hour target influences patient treatments and outcomes. This will enable policymakers (in particular, NHS Improvement and the Department of Health) to evaluate whether they want to continue to enforce this target, and to choose which target level they wish to implement, by clearly setting out whether the potential benefits of a stricter policy target (through reduced waiting times) are outweighed by increased distortions to treatment decisions (through excessive or insufficient admissions) and/or unfavourable patient outcomes. This is particularly important given the recent announcement (July 2016) that 53 trusts are temporarily exempt from the target. The planned work has already been discussed with NHS Improvement’s Emergency Care Improvement team. IFS will hold further discussions with NHS Improvement to ensure that the findings are presented in a way that enables practical implementation. Benefits to commissioning (and eventually to patient care), will therefore be achieved through increasing NHS Improvement’s knowledge of the effects of the target on patient health and hospital decision-making. (11) The 2000s saw a radical shift in policy which mandated choice for patients. This choice was enabled by changing the method of paying hospitals to activity based funding (HRGs) and encouraging the entry of private providers into the provision of services, particularly elective care for joint replacement and cataract surgery. In addition, the policy was accompanied by a large increase in the NHS budget. The short terms impacts of these policies have been evaluated (the primarily focus has been on the period 2003-2009). But this radical change has had dynamic consequences. In addition, the financial landscape has altered to one of fiscal austerity for the NHS. IFS are now in a position to evaluate (a) whether the beneficial initial effects that were seen have had longer lasting effects and (b) the impact of running this policy in a period of fiscal austerity compared to one of fiscal generosity. Establishing at system level whether the policy continues to bring benefits is extremely important for the design of future changes to the NHS and social care system. Essentially IFS need to know whether this policy has been able to maintain its initial gains or whether the policy can only deliver gains when there is spare capacity. This is very important at a macro-system level. Radical change is expensive and the burden is borne by the tax payer. IFS need to be able to establish whether this type of radical change has net costs for the healthcare system as a whole. The knowledge produced by this work would enable national commissioners and policymakers to improve commissioning by better understanding the various impacts of wide-spread choice and competition policy. (12) The NICE Clinical Guideline on Routine Postnatal Care (CG37) recommends that “All healthcare providers (hospitals and community) should implement an externally evaluated structured programme that encourages breastfeeding, using the Baby Friendly Initiative (BFI) as a minimum standard.” However, the following “Further research to evaluate the effectiveness of BFI compared to another programme, or to standard care, should be carried out.” Moreover, in a recent report commissioned by UNICEF (Renfrew et al 2012), it is clear that the strength of the evidence on the effect of breastfeeding on some health outcomes is still weak (mother: Ovarian cancer and Type 2 diabetes, child: Asthma, diabetes, leukaemia, coeliac disease, cardiovascular disease, and sepsis in the child.) The study will provide evidence on the health benefits of breastfeeding (what diseases will decreases if breastfeeding rates improve). This knowledge will benefit commissioners because it will provide them with evidence that they can use to decide whether improving breastfeeding support services (such as BFI) will improve health and reduce future health care costs. It will also benefit NICE which has called for more research on the effectiveness of the breastfeeding support services in its guidelines. Health care providers, clinicians, as well as UNICEF will also benefit because they will learn whether the BFI initiative is effective or whether improvements are necessary. IFS will disseminate their research through press releases, a IFS observation (non-technical document), seminars, and a peer reviewed journal article that is read by clinicians. IFS are in direct contact with UNICEF and will communicate directly the findings to them. UNICEF are in direct contact with UK health care providers, and changes to the delivery of natal care can be delivered through this channel. IFS will communicate the findings to NICE to improve the evidence base on the benefits of breastfeeding when they next update their guideline on routine post-natal care. (13) Research spending in the UK on health is around £8 billion annually, with £3.3bn of that being publicly funded research carried out in the university and not-for-profit sectors. However, very little is known about the productivity of this investment, and what are the determinants of innovation take-up. In response to this, the Economic and Social Research Council (ESRC) joined forces with the National Institute of Health and issued a funding call to fill this gap. The network was awarded to the IFS in the UK and the NBER in the US (http://gtr.rcuk.ac.uk/projects?ref=ES/M008673/1) in October 2014. The research specified here will allow responding to this need identified by the ESRC by studying the take-up of medical innovations. The study will provide evidence on whether less research intensive hospitals are slower at adopting medical innovations, and the cost in terms of worse health outcomes that it causes. This will provide evidence to the Department of Health and NHS England on the benefits of recommending and adopting new technologies. This will enable national policymakers and commissioners to encourage practioners to quickly adopt new treatments, improving patient health and reducing health inequalities. This knowledge will be communicated to policymakers through the existing network of IFS contacts within these organisations, and through press-released non-technical reports. IFS will also improve the knowledge base for commissioners of medical research (such as the Medical Research Council and Economic and Social Research Council), which will aid them to fund high-value research. IFS will communicate directly with contacts at the ESRC (as part of the network funding this research and wider existing contacts). (14) This work will increase knowledge of the effects of breastfeeding on child health, particularly for low socio-economic status patients. This knowledge will enable national commissioners (particularly NICE) to make clearer recommendations for breastfeeding practices. This will benefit the system by improving child health and cognitive development, and potentially reducing future healthcare and education costs. IFS will communicate this knowledge to commissioners and practitioners through press-releasing the research findings, and seeking meetings with contacts at NICE. Project (12) informs policy makers about the benefits of the UNICEF Baby Friendly Initiative, whilst project (14) informs policy makers about the benefits of breastfeeding. Moreover, project (12) informs on the benefits measured using re-admission, A&E visits, and presentation of certain medical conditions; whilst project (14) informs on the benefits measured using children¹s cognitive development as well as health measures which are independent of health care use (anthropometric measures). (15) The aim is to inform and influence policy making relating to emergency admissions in England in a way that improves patient care. By demonstrating how emergency admissions can negatively affect patient care, and evaluating the role of past and future policies on this, the research will enable policymakers (e.g. Department of Health, NHS Improvement) to consider options that mitigate the effects of emergency admissions on patient care. This would provide knowledge and evidence that speaks directly to immediate policy concerns about shortages of capacity in the NHS and progress has already begun in engaging policymakers on this issue (see existing outputs in the "outputs" section, purpose 15). References Cutler, D, R. Huckman and M. Landrum. (2004). The role of information in medical markets: An analysis of publicly reported outcomes in cardiac surgery.American Economic Review 94 (2): 342-346 Fergusson, D.M. and l.J. Horwood (1997) “Early Onset of Cannabis Use and Psycho-social Adjustment in Young Adults,” Addiction 92: 279-96. Hall, W and l. Degenhardt (2009) “Adverse Health Effects of Non-medical Cannabis Use,” Lancet 374: 1383-91. Kelly, E. and I. Rasul (2014) “Policing Cannabis and Drug Related Hospital Admissions: Evidence from Administrative Records,” Journal of Public Economics 112: 89-114. Kolstad, J., (2013). Information and Quality When Motivation is Intrinsic: Evidence from Surgeon Report Cards, American Economic Review 2013, 103(7): 2875–2910 Marshall, K.S., l. Gowing and R. Ali (2011) “Pharmacotherapies for Cannabis Withdrawal,” Cochrane Database for Systematic Reviews 1: CD008940. National Institute of Clinical Excellent. Routine Postnatal Care of Women and Their Babies. NICE Clinical Guideline 37. July 2006. Renfrew MJ, Pokhrel S, Quigley M, et al. Preventing Disease and Saving Resources: The Potential Contribution of Increasing Breastfeeding Rates in the UK. London, UK: UNICEF UK2012. Van Ours, J. (2003) “Is Cannabis a Stepping-stone for Cocaine?,” Journal of Health Economics 22: 539-54.
THE INSTITUTE FOR FISCAL STUDIES (IFS)	THE INSTITUTE FOR FISCAL STUDIES (IFS)	Patient Reported Outcome Measures	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	The Institute of Fiscal Studies (IFS) conducts independent research into the effects of economics on health and the health system with the aim to better inform policy makers, practitioners and the general public IFS have a number of research projects being conducted at once. This document numbers and describes each project aim, processing, outputs and benefits individually. (1) To understand whether and how patients exercise choice when there is no entry of private providers: the case of maternity. The aim is to address three questions: (i), are patients able to make a choice when there is very limited spare capacity in the system? (ii), if patients are able to exercise choice, what types of patients are able and willing to exert choice? (iii) do women use experience from their earlier maternities when making decisions about where and when to give birth to subsequent children? (2) To produce a model of choice that can be used to simulate and evaluate potential future policies. The focus will be on how potential policies affect where different types of patient (by age, location, or area level deprivation) are treated. (3) Comparing health care expenditure, activity and outcomes in the US and England, using the specific case study of percutaneous coronary intervention (PCI) treatment for acute myocardial infarction (AMI) patients aged 65 and over. (4) The objectives of this project are (i) to understand the impact of the introduction and expansion of the role of independent sector providers on demand for NHS-funded joint replacements and (ii) to assess how this impact varies across England, and the area level deprivation. (5) The objectives of this project are (i) to produce profiles of public-funded medical expenditure in England over the life cycle (and examine how this evolves over time), (ii) examine correlations in the concentration of medical spending over time (i.e. how much does spending on healthcare in a given year determine the amount of healthcare received in the future), and (iii) examine the share of medical spending attributed to patients in the last year of life. (6) To investigate how the demand for, and quality of NHS services have changed in areas where population has experienced rapid changes. In particular, IFS will examine whether areas with a high number or concentration of residents who are foreign born experience greater demand for two types of NHS services: (i) Accident and Emergency care and (ii) maternity services. (7) To estimate the health effects of Sure Start, a large national programme to improve early childhood development and integrate health, education, childcare, social care, and other support services to better serve families. The HES data will be used to: (i) investigate whether access to Sure Start services between birth and age 4 reduced all-cause and cause-specific hospitalisations and outpatient visits; (ii) understand the rollout of the Sure Start programme. This project can be completed with existing data. (8) To estimate the frequency of drug-related hospital admissions, focusing on cannabis-related hospital admissions, as well as admissions related to other drugs and alcohol, by region (especially TV region equivalents) and period (yearly and monthly), and in relation to demographic characteristics (such as age group and gender). These figures will then be compared to region and period specific estimates of the market size for cannabis, based on other data sources (especially sales data for tobacco-related products). This project can be completed with existing data. (9) To examine the variation in 30-day mortality rates of patient who are treated for AMI or stroke across different consultants and different hospitals. The focus will be to quantify the extent to which different consultants determine the probability of survival for patients, after taking into account the different characteristics of patients treated by different consultants, and the facilities available to consultants in each NHS hospital. This project requires the pconsult variable. IFS currently hold this variable for 2010/11 onwards, which came as part of the standard extract. This variable is required from 2003/04 to 2009/10. From May 2017 (10) IFS request linked HES-ONS mortality data to examine the impacts of the national four-hour waiting time target in NHS accident and emergency (A&E) departments. In particular IFS will examine three questions: a. Does the four-hour waiting time target change the probability of inpatient admission from A&E (e.g. are admission decisions distorted by the presence of the target)? b. What are the consequences for patient outcomes of changes in admission decisions? c. What are the consequences for the amount of resources used by hospitals due to changes in admission decisions? (11) To examine the effect of the policy shift towards choice and competition on the performance of UK acute hospital trusts. In particular the focus is on industry dynamics - which hospital trusts gained from choice and competition and which lost, and what impact did this have on the service and quality of care for their users and local populations. (12) The objective of this project is to estimate the effect of the UNICEF Baby Friendly Initiative on children’s health and health care use. The UNICEF Baby Friendly Initiative is a worldwide program that promotes breastfeeding through improving breastfeeding support services in hospitals and community services (i.e. health visiting teams). Improving breastfeeding might have effects on health and health care consumption. Although some benefits of breastfeeding are well recognised, the evidence on some other benefits is weaker. IFS will study whether the implementation of The UNICEF Baby Friendly Initiative in either a hospital or community service is associated with improvements in child and maternal health, as well as health care use. (13) The objective of this project is to understand whether hospitals that are more research intensive take up treatment innovations sooner, and whether this is translated in better health outcomes for patients and/or reduced costs for providers to achieve a given patient outcome. (14) The aim of this project to quantify the benefits of breastfeeding on children's health and cognitive development. Children born at weekends (or just before) might be less likely to be breastfed due to poorer breastfeeding support at the weekend. The project aims to use the variation in day of birth to set out the returns (in terms of patient health) to being breastfed. (15) The overall objective of the project is to evaluate how emergency admissions affect hospital production and patient outcomes in trauma and orthopaedic departments. There are three sub-objectives: (i) quantify how changes in emergency admissions have affected NHS hospitals across a range of outcomes including readmissions, cancellations of elective surgery, and length of stay; (ii) compare how the relationship between emergency admissions and these outcomes has changed in response to past NHS policies including Payment by Results, Referral To Treatment targets and NHS Choices; and (iii) assess how future policies relating to ambulance referral patterns and hospital closures may impact the relationship between emergency admissions and these outcomes.	Only substantive employees of IFS will have access to the data and only for the purposes described in this document. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide. All processing of ONS data will be in accordance with standard ONS Terms and Conditions. All projects are underwritten by the ESRC Centre for Public Policy at the IFS. In addition, there are some additional funding streams. 1,2 and 4 are covered by ESRC through the principle researchers ‘Future Leaders’ grant 3 and 13 are funded by an ESRC funded grant on Health Network 7 is funded by the Nuffield Foundation IFS confirm that none of the funders exert any influence over the projects and outputs. No data (other than aggregated data and small numbers suppressed) will flow outside the UK. This is particularly in relation to projects 3 and 13, but also applies to all the other projects described The IFS will not attempt, nor have a requirement to re-identify individuals in the data supplied by NHS Digital Processing activities for each project: (1) Part 1 will model changes in the probability that a woman gives birth in her nearest maternity hospital) over the past decade. IFS will assess whether women with certain characteristics (older, or from different types of areas) are more likely to bypass their nearest hospital, or whether patients travel further for maternity care at a teaching hospital. Part 2 will focus on patients who have at least two HES maternity records, and examine whether care offered during the birth of the first child affects the mother’s choice of hospital for subsequent births. There are a range of factors that could affect both the ease of childbirth and where a mother decides to give birth to subsequent children that are unrelated to the care she received, for example, her age. IFS will therefore isolate random variations in treatment, which could be used to identify the impact of health care provision. Possible examples include whether the mother gives birth at a weekend or public holiday, the number of other babies born at the same hospital on the same day. The data will be used to create a sample of women who gave birth in NHS hospitals. The pseudonymised ID will be used to identify whether, where and when these women give birth again. The data will then be used to estimate statistical models to assess which factors around the time of the first birth affected the subsequent patterns that are identified. All work is conducted using the statistical software package Stata. The HES data may be linked to aggregated geographical data relating patient or hospital. Examples include the number of women of childbearing or number of hospitals within the local area. These data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model. An example of a publicly available geographical data to be linked would be ONS population statistics (http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-315018). HES data may be linked to aggregated data at the level of the hospital (for example, whether the hospital has an alongside maternity unit) or the geographical area of the patient/hospital (For example the number of hospitals within the local area). Again, these data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model. IFS would like add extra characteristics of the hospitals, as these characteristics might affect the hospital choice of mothers. (2) Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences). As it is expected that clinical need will be a crucial input into individual patient decision making, the HES and PROMS datasets will be linked. This linkage will enable the production of a model that takes patient need into account and therefore provide more accurate analysis and predictions. PROMs data are also required over this period in order to understand how the clinical benefits following joint replacement surgery has changed over time, particularly in light of the independent sector reforms. This is essential for an analysis which estimates the implications for population health following the huge increases in the volume of joint replacements observed in the ten years prior (i.e. IFS can examine whether the clinical benefit for patients has increased or decreased as a result of greater availability of hip and knee replacements. This will help to analyse whether the independent sector reforms have increased the welfare of NHS patients). (3) IFS will calculate the percentage of patients receiving a PCI within 1 day and within 30 days of a first admission for an AMI, and compare these rates to those in the United States. IFS will also calculate these rates by region and PCT/CCG. These data will not contain rates based on fewer than 50 patients to ensure that the data are not disclosive. It will be necessary to calculate rates back to 2000 to understand whether the countries have been converging or diverging overtime as the treatment became more widespread in the early 2000s. IFS require the most recent data as the preferred journal requires data from the past 5 years to be included. As England had a large expansion in 24/7 PCI centres after 2008 following The National Infarct Angioplasty Project (NIAP), IFS have collected the months and years that each centre opened. IFS will document the resulting changes in PCI rates, and use these changes to understand how PCI and the NIAP proposals have affected patient outcomes, such as 30 day in hospital mortality and readmissions for subsequent AMI. This analysis will allow IFS to understand both the effectiveness of the NIAP proposals in England, and to understand the extent to which this roll-out explains the falling AMI mortality differences between the United States and England. IFS will not publish any statistics using HES that link to particular hospitals, or samples of patients fewer than 20. (4) IFS will model the relationship between the total number of NHS funded elective hip replacements in a middle super output area in a given year, and the introduction of Independent Sector Providers. IFS will compare the same area over time, and compare across areas by distance to the nearest independent sector provider offering hip replacements, relative to the nearest Acute Trust providing hip replacements. Data will be combined with publicly available area level and GP practice characteristics, in order to examine variation in outcomes or behaviour, or to control for potentially confounding factors at the area level. Publicly available area level data includes measures of population size and levels of deprivation. These variables are used in area-level regressions as control variables. This includes population data (available from the Office for National Statistics and local deprivation scores made available for public use by the Department for Communities and Local Government). IFS require data back until at least 2000, as the policy to increase and formalise the role of the Independent Sector began in 2003, and the ability to study a comparison group is essential to accurately identify the impact of the policy. (5) IFS will examine the age profile of English hospital spending across the period between 1997/98 and 2013/14. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for all inpatient activities to different age groups. Using publically available data on the English population, the average spending per individuals of a given age can be derived. IFS will then examine how this develops over time, providing evidence on whether average spending for individuals of a given age has changed over time (i.e. is the average spend for a 70 year old male in 1997/98 different to a 70 year old male in 2013/14). The pseudonymised HES indicator will be used to track the use of hospital care for a random sample of individuals in each year of the data. This will allow an estimate of total healthcare expenditure for individuals over the entire period, providing a measure of “lifetime medical spending” for older individuals. It also allows IFS to examine the correlation between health spending in one year and another (i.e. does health spending in one year predict health spending in the next, or five years later etc). Individuals who die in hospital are recorded in HES (through the discharge method variable). For individuals who die in hospital, IFS can examine the amount (and cost) of hospital care received in the final year(s) of life. In this way, IFS can estimate the amount and the share of hospital expenditures that are incurred in the final year of life. These estimates can then be compared to the findings of other researchers who are conducting a comparable analysis on similar data in other countries such as the USA and other European countries (Note: IFS will not combine the data with these other researchers, but only examine regression coefficients and the findings of this research). IFS require data back to 1997/98 to provide the longest time series possible over which you can track individuals using the HES identifier. This will (i) provide the largest history for individuals (and therefore acts as the best proxy for lifetime use of the service) and (ii) provides a significant period of time over which to examine how the distribution of spending across ages has developed (i.e. IFS can examine whether the average 70 year old in 2013/14 uses more healthcare than a 70 year old in 1997/98). (6) IFS will model the impact of rapid immigration on the demand for accident and emergency services by comparing the change in (i) inpatient admittances for ambulatory care sensitive (ACS) conditions and (ii) visits to A+E, across local authorities with different changes in the concentration of foreign born residents (population data at the local authority level is sourced from the publicly available UK Labour Force Survey). Admittances for ACS conditions are derived from OPCS-4 codes. A similar exercise will be conducted with admittances for maternity patients, comparing the number of birth episodes recorded by inpatient HES across these regions. IFS will then also examine the number of 30 day readmissions for newborn children across these areas, using the pseudonymised HES identifier, to examine whether the quality of maternity care has deteriorated in an observable way in areas where the population has rapidly grown. This will provide evidence on whether NHS trusts adapts quickly to changes in the size and the characteristics of the population which they treat. HES A&E data is required for the most recent period of time in order to understand the use of the service during a period which has witnessed significant changes in the size, and composition, of the English population. This provides sufficient variation in the data to attempt to estimate causal impacts of population change on demand for, and quality of, A&E services. (7) The same personnel who currently process data for projects involving all HES records will create a dataset that contains only admissions for individuals under the age of 30. The dataset will be placed in a separate secure area for the project team to use, so that they are able to access only the data needed for the project. To investigate the relationship between Sure Start and hospital admissions, IFS will merge information on the location of Sure Start Centres into HES using LSOA identifiers. IFS will then test whether cohorts exposed to Sure Start (both overall and accounting for intensity of exposure) are less likely to experience hospitalisations and outpatient visits (all-cause and cause-specific) and A&E admissions (from 2007-08 onwards). The two sets of treatment and control groups will be compared: those who lived at ages 0-4 (i) in areas that implemented Sure Start earlier vs. later and (ii) in areas that experienced larger vs. smaller expansions of Sure Start. To understand the roll out of Sure Start, IFS will examine the determinants of the timing (the year of opening of the first Sure Start Centre in a given Local Authority (LA)) and the intensity (the number of Sure Start Centres in a given LA per year) of the rollout. This will include pre-programme levels and trends in hospitalisations and outpatient visits (all-causes and cause-specific) among the determinants. (8) The same team who currently process data for projects involving all HES records will create a dataset that contains only drug related admissions. Episode level data on hospital admissions will be used to compute the frequency of drug-related hospital admissions. This will focus on cannabis related hospital admissions as well as other drug-related and alcohol-related admissions, and will be computed by time period (monthly and yearly) and region (especially TV region equivalents, the regional level at which the tobacco sales data are available) , and in relation to demographic characteristics (such as age group and gender). The output of the analysis will be aggregate data (by region, time period, and for demographic groups), with small numbers suppressed in line with HES analysis guide. This will then be compared with the incidence of cannabis-related hospital admissions to estimates of cannabis market size, which are constructed from other data sources (especially sales data for tobacco-related products). Overall, this will allow IFS to compare market-size estimates to admission based estimates of heavy drug consumption for cannabis, as well as other drugs and alcohol, and to test the relationship between these variables across regions and over time. As the sample sizes are likely to be small, IFS will ensure that only aggregate numbers are reported and any small numbers are suppressed. (9) IFS will use episode level data to compare 30-day in-hospital mortality rates of patients treated by different consultants following admission to an NHS hospital for an AMI or stroke. Admittances for AMI and stroke are derived from ICD-10 diagnosis codes contained in HES. Consultants are assigned to patients in HES using the anonymised consultant ID (variable ‘pconsult’). Patients who die in hospital are recorded in inpatient HES (through the discharge method variable). Anonymised patient IDs will be used to examine whether patients who are discharged but then readmitted during the 30 day period following the initial admission die in a subsequent hospital spell. The analysis requires the construction of detailed control variables to account for differences in the underlying health of patients treated by different consultants and hospitals. Failing to account for these differences will lead to inaccurate estimates of the effects that each consultant has on patient outcomes. Detailed measures of health conditions and past hospital use are therefore essential for this analysis. IFS will derive a range of clinical indicators using the ICD-10 diagnosis codes in HES, and use these to create the Charlson Index to capture patient morbidity. Using data from 1997/98 – 2014/15, IFS will use the anonymised patient identifier to track patient inpatient admissions and outpatient attendances over time in order to construct detailed histories of patient hospital use. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for each of these activities to summarise past hospital use. IFS will also create a variable for each year which indicates whether a patient has been treated for a heart attack or stroke in a previous year. Previous research has shown that a major determinant in survival following a heart attack is the amount of time that elapses between onset and treatment, and the distance that patients need to travel to reach a hospital for treatment. This will be addressed by examining the distance between the Lower Super Output Area of patient residence and the hospital in which they are treatment. In addition, for the period 2007/08 – 2014/15, IFS will use the Accident and Emergency data to examine whether the onset occurred at home (variable ‘aeincloctype’) and the time that elapsed between arrival at hospital and admission (variable ‘tretdur’). In order to separately estimate the impact of consultants from the hospitals in which they work, the analysis needs to control for differences in the types of patients treated by different hospitals. It also requires the observation of consultants working in different hospitals over time. IFS will address the first point by combining publicly available aggregated geographical data relating to the socio-economic status and population health to summarise the characteristics of the patient population served by each hospital. Inpatient HES data will also be used to create other indicators of patient health and quality of local primary care (e.g. the admission rates for ACS conditions in the local area), and the quality of other care provided in the hospital (e.g. hospital level readmission rates for elective hip replacements). In addition, for the period 2007/08 – 2014/15, Accident and Emergency data can be used to separately analyse the outcomes for patients who arrived at the hospital in an ambulance (contained in variable ‘aearrivalmode’). This would allow analysis on a subset of patients for which it is certain that patient did not choose the hospital in which they were treated, and therefore rules out matching of (otherwise unobservably sicker) patients to hospitals which could potentially bias results. The second point is addressed by following consultants across hospitals over time, through the use of anonymised consultant team variable (‘pconsult’). This allows a comparison of patient outcomes when treated by the same consultant but in a different setting. IFS require inpatient and outpatient data back to 1997/98 for two reasons. First, the analysis relies on consultants moving across hospitals over time. Using the longest available period of data will capture substantially more movement in staff across hospitals, and will maximise the sample of patients whose outcomes can be studied. This will increase the precision of the estimates. Second, the panel element of the data will be used to construct detailed histories of hospital use for patients. This will improve the accuracy of the analysis by controlling for a broad range of factors in the underlying health of patients. IFS requires A&E data back to 2007/08 to supplement the analysis of inpatient and outpatient data. Using the full period of available data will maximise the number of patients for which full information on care received from arrival at hospital to discharge is available. Information on the use of an ambulance will provide a subset of patients who IFS can reasonably assume have no choice over the hospital they attend. IFS will the conduct the analysis for the period between 2007/08 and 2014/15 both with and without use of the additional information of the method of transport to examine whether patients who do not use ambulances selectively sort into particular hospitals. The analysis can then be extended to earlier years (prior to A&E data availability) with a better understanding of whether patient sorting between hospitals occurs. The analysis on the role of independent sector providers within the NHS requires inpatient HES data up to 2013-14. When examining the impact of these reforms, it is essential to understand whether the trends seen between 2000-1 and 2010-11 continue between 2010-11 and 2013-14. This is a period of time in which (i) NHS funding was relatively restrained and (ii) the wider economy was recovering from a large recession in the preceding years. As a result, to evaluate the impact of the reform on patient health and the quality of NHS services provided, it is crucial to examine the longer term impacts of the reforms. For the work on heart attack treatment, IFS intend to submit this work to the Journal of the American Medical Association, which has a strong preference for work that covers the past 5 years. Importantly, in all cases the ability to conduct research on the most recent years of data is crucial for the analysis to be both timely and relevant. This maximises the impact of this research on feeding into current policy debates such as the extent to which private providers are used within the NHS, and how the NHS has responded to the challenges posed by a growing and ageing population. All outputs from each project will contain only aggregate outputs with small numbers suppressed. IFS report aggregate summary statistics (i.e. total number of women giving birth in NHS hospitals in 2010/11) and regression coefficients from large-sample regressions. No record level data will be shared with third parties. Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences). From May2017 (10) IFS will use the A&E HES data from 2010/11 - 2015/16 (at record level) only to study whether the probability of inpatient admission changes for patients who are admitted during a period close to the four-hour waiting limit. This will be achieved by computing the counterfactual probability of admission in the absence of the target. IFS will calculate this counterfactual by estimating a polynomial regression (regressing admission on the period of admission) for all patients who are feasibly not affected by the target (e.g. all patients admitted/discharged from A&E between 0 – 180 minutes, and after 240 minutes). IFS will use data on patient characteristics and investigations/symptoms contained in the A&E data, along with diagnosis codes for admitted patients (in their APC HES records) to examine how the characteristics of patients vary with time. APC HES records will be used to examine how treatment intensity varies across patients who are admitted at different points of time. For example, IFS will examine whether length of stay for inpatients admitted after a shorter period of time in the A&E department are different from those who are admitted after waiting for longer. IFS will also examine how the probability of readmission to hospital varies across these individuals. IFS will use the linked HES-ONS mortality data (at patient record level) to examine whether the probability of death varies across individuals who are admitted or discharged after different waiting times. In-hospital mortality could be recovered from unlinked HES. However, out-of-hospital mortality may also change as a result of the policy. The inclusion of the ONS mortality data would therefore allow all-location mortality outcomes to be examined. IFS will also examine the underlying cause of death, to investigate whether cause of death is the same as the major diagnoses when patients recently visited hospital. (11) IFS will construct trust level measures of quality of care for different services at annual level between 2000 and 2014. These measures of service quality will be related to changes in admissions for these services. IFS will examine changes over a long period to allow them to follow dynamic effects of the policy shift towards competition and choice. IFS will focus on specific services which have been examined previously in international and national studies, and include treatment for the following conditions: Acute Myocardial Infarction (AMI), heart failure, pneumonia, and hip and knee replacements. For each condition, IFS will create site and trust level measures of outcome quality (risk adjusted 30-day survival rates and risk-adjusted 30-day readmission rates), and condition-specific measures of process of care which capture the hospital’s conformance with established clinical guidelines for care (e.g. for AMI this includes the administering of aspirin, ACE inhibitors, smoking cessation advice, beta blockers, and angioplasty). Patient data at the record-level will be required to (a) construct risk adjustors and (b) to allow separate analysis of the data by local-area deprivation status (measured by IMD), in order to examine whether patients of lower Socio-economic status (SES) gained less or were harmed by these policies. This is an important component of the analysis and hence the need for HES record level data. IFS will also match nationally published data on quality and processes of care from other sources at the trust level. These sources include data from MINAP (for treatment of AMI and heart attack patients); the health care regulator; published trust level PROMS data (for joint replacements); staff satisfaction (from the annual staff satisfaction survey) and patient satisfaction. These data will not allow IFS to examine within trust across patient SES type differences as they are aggregate data at trust level. (12) Data on UNICEF Baby Friendly hospitals (including the date in which the status was acquired) will be obtained from UNICEF. The data will be merged with individual-level records for babies from inpatient HES, using the site code (procode5) to identify hospitals. IFS will estimate multivariate regressions of health outcomes and care use (re-admission, A&E visits, presentation of certain conditions) to examine whether hospitals that are compliant with the UNICEF Baby Friendly Initiative deliver better patient outcomes. The use of hospital fixed effects will allow IFS to control for endogenous placement of the UNICEF initiative (e.g. better or worse hospitals are systemically enrolled in the initiative). Publicly available information on the socio-economic status of the population at the local area level (e.g. the ONS created Index of Multiple Deprivation at the Middle Layer Super Output Area) will be merged with the hospital data to capture differences in patient characteristics across areas. HES records will also be merged with data IFS have collected on the community service providers that comply with the UNICEF Baby Friendly Initiative, using the postcode of the community service and the postcode of the General Practitioner of the patient. IFS will then conduct the same type of analysis previously outlined at the hospital level for community services. (13) IFS will use publicly available bibliographic sources (e.g. PubMed, Web of Science, academic journal websites) to calculate the number of publications authored each year by staff employed by each NHS trust. This will be merged with hospital-level inpatient and outpatient HES data and A&E records to identify key medical treatment innovations in the period between 1997 and 2015. IFS will use this to estimate whether the health outcomes (e.g. mortality, readmission, A&E visits) of patients who are treated for conditions associated with these medical innovations improved more rapidly over time in research-intensive hospitals (as measured by the number of topic-specific publications) (14) IFS will calculate the 30-day hospital readmission and in hospital mortality rate for children born on each day of the week, using inpatient HES data from September 2000 to August 2001. IFS will then uses these figures to compare readmission and in hospital mortality rates for children born Friday - Sunday, to children born Monday - Thursday. Only aggregate figures will be published (by day of the week), ensuring that large sample sizes are used in all cases. This will be used to supplement existing work on this project which makes use of the Millennium Cohort Study, 2005 Infant Feeding Survey, and 2007 Maternity Users Survey (note, IFS will not link individual level HES records to individual records in any of these survey (Millennium Cohort Study, 2005 Infant Feeding Survey , 2007 Maternity Users Survey). (15) The project will use inpatient data for the years 1997 to 2010 and A&E data for the years 2007 to 2010. Data will be extracted for trauma and orthopaedic departments, identified using consultant specialty information. Sub-objectives (i) and (ii) will involve building statistical regression models that compare the number of emergency admissions at each hospital with patient outcomes at that hospital. Sub-objective (ii) will assess how this statistical relationship has changed over different time periods and hospitals, for example before and after the introduction of Payment by Results in 2004. Sub-objective (iii) will involve linking the inpatient and A&E data to establish which patients at trauma and orthopaedic departments arrived by ambulance. Statistical models will then be used to evaluate how the number of emergency admissions, and the associated impact on patient outcomes, would change if ambulances were to assign patients to hospitals differently or if certain A&E departments were to close. This application covers data from 1997/98 to 2016/16. It is important to have data that covers this period for three key reasons. First, a number of the research aims are to investigate the impact associated with different policy changes that have taken place during this period. In each case, IFS need data from before and after the policy change. For example, project (4) studies the impact of introducing private providers into the NHS market for elective care, which took place as part of a set of reforms throughout the 2000s. Having data from before, during and after this period is essential in understanding the changes that took place as a result of these reforms. Second, data from 1997/98 to 2016/17 will provide the longest time series possible. This will allows IFS to better understand trends in NHS activity over time (e.g. in project (5) this allows IFS to examine how NHS activity has developed across birth cohorts, and in project (11) to examine whether the impact of competition has changed over this period. Finally, using data from multiple years helps to maximise sample size. This is crucial in boosting the statistical power of the research, helping to accurately identify and estimate effects. As a result, these data requirements are essential in allowing IFS to carry out the proposed research.	(1) There will be three written outputs: (i) an IFS working paper produced in the next year, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care. Staffing constraints have meant that progress was slower than anticipated. However, the work has been discussed with the Royal College of Midwives and the North East London Sustainability and Transformation Planning Team. The results will be presented at a one-day event in September aimed at policy-makers. Written outputs are expected to follow in late 2017 and 2018. (2) The outputs for this project are similar to those for project (1). The aims are to produce written outputs that are widely cited in the academic literature and encourage more academic work on the NHS, and non-technical summaries that will provide information for policy makers that would otherwise be unavailable or very costly for the Department of Health or Monitor to acquire. As this project is more complex, IFS expect outputs in the next 1-3 years. The model IFS produce will have the capacity to model potential policy scenarios. IFS will interact with the Department of Health to ascertain whether there are any policy scenarios they would like IFS to model to maximize the value to the Health and Social Care system. IFS has presented the work at the Royal Economic Society and European Economic Association conferences, and discussed finding with the Competition and Markets Authority. A working paper is complete. This will be published in June 2017, when the paper will be submitted to a journal. (3) There will be two sets of outputs from this work. It was intended that the first set of work would relate to PCI use in England and the US, comparing same day and delayed PCI rates, and volumes per facility. However, this work has been on hold due to data delays in the US. The second set of work will examine the impact of the roll out of PCI centres in England on patient outcomes. Outputs will include submission to a economics journal, such as the Journal of Health Economics, and an IFS briefing note and press release. These are still on track to be delivered in the second half of 2017. Given the direct policy-relevance of this analysis, IFS will contact Department of Health, NHS England, and NICE in order to present and discuss the findings, to ensure that they are aware of the results and to check the validity of any assumptions that have been made. (4) Some of these outputs from this project, including several conference presentations, a policy presentation and a non-technical policy summary, have already been produced under the previous license agreement. Previous conference presentations included a workshop attended by representatives from the Department of Health, Monitor, the Nuffield Trust, the Office for Health Economics, and the Kings Fund, and economics academic conferences including the Royal Economic Society conference. IFS submitted an article to the Journal of Public Economics, the leading academic publication on public economics (including health economics) in early 2017. IFS are currently waiting for a decision from the editor. (5) The principal outputs are (i) a working paper, which was published under the IFS working paper series (see project 1) in August 2015 (https://www.ifs.org.uk/uploads/publications/wps/WP201521.pdf), (ii) an academic conference presentation in March 2015, (iii) a peer-reviewed journal article in the economics journal Fiscal Studies, which was published as part of a special issue of Fiscal Studies on cross-country comparisons of health spending across the lifecycle in November 2016 (http://onlinelibrary.wiley.com/doi/10.1111/j.1475-5890.2016.12101/full), and a non-technical, policy summary (https://www.ifs.org.uk/publications/8737). IFS will present the results in dissemination events with policymakers, funded by the Economic Social and Research Council in September 2017. Fiscal Studies is a peer-reviewed economics general with all articles explicitly aimed at bridging the gap between academic research and policy, with a reputation for publishing timely high-quality articles that are easily accessible to policymakers. A workshop to discuss preliminary findings took place in March 2015. This workshop was attended by representatives from the Department of Health, who subsequently invited IFS to present the findings at the Department. IFS have spoken to the OECD about this work, who believes it could help inform their highly influential work on cross-country comparisons of health systems. The ESRC event will be exclusively focused on policymakers, with invites to representatives of DH, Monitor and PCTs, along with members of influential health policy research groups such as the Kings Fund and Nuffield Trust. IFS expect outputs from this project in 2017. (6) The principal outputs will be (i) academic conference presentation at the European Economists Association (EEA) Annual Conference in August 2015, the Royal Economic Society (RES) Annual Conference in March 2016, the Intertnational Institute for Public Finance (IIPF) in August 2016, and the European Association of Labour Economists (EALE) Annual Conference in September 2016, (ii) a working paper, published under the IFS working paper series, , and (iii) the submission of a peer-reviewed journal article to the Economic Journal. The benefits of these outputs are discussed above. Output (i) has already been realised. The EEA, RES, IIPF and EALE Conferences are attended by the leading economists in Europe and the USA, and in light of comments received at the conferences, the work on this project has been extended. As a result, the original target date for the publication of the working paper and journal submission was pushed back from Spring 2016 to Summer 2017. IFS have produced a non-technical policy summary for policymakers. This has been sent to the Health Foundation for comments, and will be sent to representatives of Department of Health, Monitor and NHS England in the next few months. (7) In the first year of the project, the main outputs will be an interim report submitted to the funder: due in July 2016 on the impact analysis of the determinants of the rollout of Sure Start. The initial project planned specified that in the second year (2017), IFS would have three main outputs: (1) the final report, to be submitted to the Nuffield Foundation in February 2017; (2) an IFS working paper (see above), and (3) a related academic paper. The timeline of this project has been pushed back a year after delays in receiving essential non-HES data for the wider project (an application for local area level Health Survey for England data was submitted to NatCen and the Health and Social Care Information Centre in 2015, and is ongoing). Outputs (1), (2) and (3) are now expected in 2018. Both report and paper will be available on the IFS website. The academic paper will be targeted to a top economic journal, such as the Economic Journal (see above). The findings of the report will be disseminated by press release and an IFS policy observation (on the IFS website) in order to reach target audiences in the media and general public. A launch event will be organised at IFS, where the results will be presented and academics will be invited (experts in early years policy) and policy makers (MPs working on early years policy of the All Party Parliamentary Group) to discuss their implications. Finally, IFS will present the findings at major economics conferences (such as the Royal Economic Society, RES), to gain comments from other academics working in the same field before submission to a top-tier peer-reviewed economic journal. (8) IFS expect written output within the next 1-3 years. This will take the following form: an academic economics journal article submitted to the American Economic Journal: Economic Policy (AEJ-EP) or the Economic Journal (EJ). Both of these journals are leading international peer-reviewed Economics journals. The principal audience is economics academics who will read and cite the paper. Other outputs will include presentations at academic conferences and seminar presentations, which will focus on receiving comments from economists on how to improve the analysis. (9) The project is expected to produce a range of outputs, including ((i) multiple academic conference presentations to general economics (e.g. the Annual Royal Economics Society conference) and health economics audiences (e.g. the annual meeting of the UK Health Economists Study Group); (ii) an IFS working paper (see above); (iii) the submission of a journal article to a leading peer-review economics journal, such as the Review of Economic Studies (Impact Factor: 4.038) or the Journal of Health Economics (IF: 2.579); (iv) a non-technical policy summary, which will be press released and sent to contacts at the Department of Health and NHS England. The Health Economists Study Group is a work-in-progress conference attended by the leading health economists in England, and representatives from NHS England, the Department of Health and leading health policy organisations such as the Health Foundation and the Kings Fund. Their comments will give the researchers the chance to improve the analysis and focus the findings in the most informative way for policy. Output (i) has been realised in 2016 and 2017, with a number of presentations at leading UK universities (UCL, King’s College London, Oxford) and a conference presentation at the International Institute for Public Finance (IIPF) Annual Conference planned in August 2017. Outputs (ii) – (iv) have been delayed while in discussion with NHS Digital about access to workforce data. IFS aim to produce these outputs in 2018. In the first year, the main outputs will be presentations and discussions with the Department of Health and NHS England to check the validity of the assumptions underlying the model that IFS estimate, and to identify where the model could provide information and simulations that are useful to policy-makers. As the estimation of the model is reasonably complex, written outputs are expected over the next 1-3 years. These will take three forms: (i) an IFS working paper, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care. From May 2017 (10) The analysis will be used to produce a range of outputs.Three types of written output are expected: (i) a working paper, published as part of the IFS working paper series, (ii) a peer-reviewed journal article submitted to a leading peer-reviewed economics journal; (iii) a non-technical research summary which will be press-released, and shared directly with NHS Improvement and the Department of Health. This work has already been discussed with economists at NHS Improvement who are also working on projects to examine the quality of care in A&E departments. Other outputs will include presentations at academic conferences and to government departments. IFS would aim to produce a working paper and a policy report by December 2017. Articles in economics journals typically take 2-3 years from the start of the work to publication in a peer-reviewed journal. IFS would therefore expect an article to be published in 2018 or 2019. (11) The expected outputs are similar to (1), with the publication of a working paper, submission to a peer-reviewed health/economics journal, and a non-technical summary aimed at policymakers. (12) There will be three written outputs: (i) an IFS working paper produced in the next year, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to an internationally recognized peer reviewed economics journal; (iii) a non-technical research summary which will receive a press release and target policy makers from UNICEF, Department of Health, Monitor, NHS England and the CQC. Other outputs will include presentations at academic conferences and presentations to policy makers involved in the planning and delivery of NHS care. (13) There will be three types of written outputs: (i) a working paper, (ii) a submission to a peer-reviewed economics journal and (iii) a non-technical report to disseminate findings to interested policymakers and funders of national research, particularly the Medical Research Council and the Economic and Social Research Council. Outputs (i) and (iii) will be produced in 2017. Publication in a peer-reviewed journal will occur in 2018 or 2019 (due to long publishing times). IFS will also present the results to the ESRC-National Institute of Health (US) funded network on the value of Medical Research. This network meets twice a year in either the UK or the US and includes economists and medical professionals interested in the field of medical innovation. (14) There will be three written outputs: an IFS working paper (this will update a previous version that did not include HES data), (ii) a resubmission to a leading economics journal (a previous version, without HES data, was submitted in September 2015. IFS have been invited to resubmit a new version), and (iii) a non-technical report discussing the results of the work in order to benefit policymakers, and other entities interested in breastfeeding, such as UNICEF and the National Childbirth Trust. (15) This research project, which was previously under another data agreement, has already produced a number of outputs. This has included presentations at the Royal Economic Society, University of Manchester, University College London and Northwestern University, as well as a series of discussions with NHS Improvement and a range of NHS employees (managers, physiotherapists and nurses). These existing outputs relate to sub-objective (i), and the work will now be extended to sub-objectives (ii) and (iii). Planned outputs include a draft research paper for circulation in September 2017 and further presentations that are currently being scheduled between May 2017 and January 2018 (at various conferences, universities and at NHS Improvement). Following this the research will be published as a working paper (available via the IFS website) and submitted for publication to an academic economics journal in late-2018. All outputs will be in aggregate and comply with the HES Analysis guide for small number suppression.	The existing work using HES data has been used to inform policy maker Monitor, NHS England, the Department of Health, the Cabinet Office and representatives from PCTs. As noted by the Department of Health, academic work helps to understand the impacts of former policy and how to improve the existing health and social care system. Examples of previous outputs, and the steps IFS have taken to disseminate the findings to policymakers, include: •The research report “Choosing the place of care” was published in 2012. A summary presentation has since had 9,505 views online. After the report was published, representatives from Monitor, the Department of Health, and NHS England met to discuss the policy implications of these findings. In the months that followed, economists from the cabinet later requested a meeting to ask for advice on NHS competition. •The research report “Public pay and private provision” was published in 2013. The accompanying presentation has now been viewed 16,342 times online. The results were presented at the Nuffield Trust’s Competition for Care conference in May 2013, which also included speakers from the Competition and Cooperation Panel, Monitor, NHS England and the NHS Confederation. The audience included both national policy makers and local commissioners. The results have also been presented to a meeting of Conservative Health at the House of Commons in July 2013. •‘Policing Cannabis and drug related hospital admissions: Evidence from administrative records’, an article in the Journal of Public Economics (released April 2014). The Journal of Public Economics is a highly respected peer-reviewed economic journal. The article has 7 citations thus far. • IFS have invited representatives from the Department of Health, NHS England, and Monitor to two workshops on academic findings on health care in 2013 and 2015, which they have chosen to attend. This indicates that academic work in general, and this work specifically, is valuable to them. Following the 2015 conference, IFS were invited to present the results to the Department of Health, further indicating the importance of this research. Following discussions with DH delegates, IFS are planning to modify their research (distinguishing between spending on elective and emergency care), as it was suggested that this could be useful to the Department during the upcoming spending review. Future projects will have a range of direct benefits to health and social care over the coming years: (1) Results from this project will improve the understanding of how and why patients make choices, and more specifically, how women respond to the quality of maternity care. This will benefit the health and social care system in two ways. First, it will assist policy makers in deciding how effective patient choice is in promoting competition between hospitals and therefore driving up standards. Second, it will inform Acute Trusts about how women make choices about where to give birth, and the potential financial consequences for Trusts who lose patients as a result of providing poor quality care. The interim findings of the project (along with those relating to project 6) were shared and discussed with representatives from the Royal College of Midwives in July 2016, and the North East London Sustainability and Transformation Planning Team in December 2016, and will inform NELST going forward on staffing decisions in the fact of increased patient demand. These benefits will be realised over the next 3 years (2) The model of hospital choice can be used to model how patients respond to potential policies, such as the reorganization of hospital services, or hospital mergers. The focus on equity is in line with NHS England principles of promoting equality and equity in provision, and the objectives of the Department of Health (see attached letter). IFS will liaise with the Department of Health to understand how the model could be more useful to them. Again, these benefits will accrue over the next three years. (3) The government has previously used cross-country comparisons to assess the effectiveness of the health care system. The results will aid such comparisons. The results will also provide information that can be used to evaluate the implementation of NICE guidance with respect to the treatment of heart attack patients between 2008 and 2010, which recommended the use of Percutaneous Coronary Intervention for certain patients. (4) Policies of the previous two parliaments have increased the role of the private sector in delivering NHS funded care, yet there is very little evidence on the impact of these providers on the impact for NHS funded care. This research will help inform policy makers of the potential effects on patient demand, health care supply, the financial implications for NHS providers, and the equity of provision if the role of these providers is expanded in the future. These issues are of importance to the Department of Health, Monitor, the Cabinet Office and NHS England. Thus far, this has been demonstrated by both the Cabinet Office and NHS England requesting updates to this work, indicating that this work has the potential to feed into policy-making in the short to medium term. A working paper version of this research was warmly received by a number of representatives from Monitor, including the Economics Director, Cooperation and Competition at Monitor, when presented in September 2013. (5) Understanding how population health care needs are likely to change is important for both national policy makers and local commissioners, particularly given tighter NHS budgets. The Department of Health has already shown interest in this work, part of which IFS have been invited to present to the Department in June. The aim is that the findings will also feed into initiatives such as the Better Care Fund and help to achieve efficiency savings set out by the QIPP initiative and NHS England’s Five Year Forward View. This research is supported by the Department of Health, who acknowledged its vital importance and provided supporting evidence for IFS funding applications to the Health Foundation. Representatives from the NHS England Strategy Group have also indicated to IFS the importance of such work in informing future policy when meeting to discuss the work. (6) Changes in the volume and composition of the population have important implications for the quantity and type of health services demanded by patients. This work will show how rapid population change can affect demand for services, and therefore help CCGs and Acute Trusts plan for the future. This could be in terms of how to organise primary and secondary care services or how to determine future staffing levels. The focus on A&E services should be particularly useful, given the difficulties experienced by A&E departments this past winter. These benefits will accrue over the next 3 years. IFS have already discussed findings with the North East London Sustainability and Transformation Planning Team. These discussions (also relating to project 1) helped NELST to internally review their provision of maternity care (underway in December 2016) following large increases in the number of mothers seeking care at their hospitals. (7) The analysis of Sure Start will provide a detailed and thorough cost-benefit analysis of the programme. Sure Start was funded at £1.8 billion in 2010-11 and accounted for a third of government expenditure on early years programmes. Funding per eligible child fluctuated over time but averaged about £6,500 per year. This reflects a considerable government investment in early years interventions, and it is important to understand whether this intervention provided value for money by improving subsequent outcomes. One of the major rationales for Sure Start is the evidence from other countries that early intervention is more cost-effective than treatment for poor child health. There has been a renewed focus on early intervention in the UK; for example, a 2012 report by the Chief Medical Officer (CMO) called urgently for more investment in early years to prevent poor outcomes later in life. Similarly, the NHS Five-Year Forward View (2014) called for a ‘radical upgrade in prevention.’ The government has made explicit plans to deliver part of this early intervention through Sure Start; for example, the 2009 child health strategy ‘Healthy lives, brighter futures’ envisages a strengthened role for Children's Centres in improving children's health and supporting parents from pregnancy onwards. Poor child health generates substantial costs for children, their families and the Health and Social Care System . Research by Action for Children and the New Economics Foundation suggests that, over a 20-year period, preventable health and social outcomes faced by children and young people will cost £4 trillion (‘Backing the Future’, 2009). Even the short-term costs imposed are considerable; for example, the short-term hospital costs of severe unintentional injuries to children are estimated at up to £87 million per year (CMO’s Annual Report 2012, Chapter 3, p. 8). Potential long-run costs could be in excess of £2 billion (ibid). Using HES to understand whether Sure Start is an effective way to reduce the rate of these hospitalisations is a concrete example of how IFS hope to add value to the health and social care system. Understanding the role of community based health services is vital in designing new models of care that both deliver better value to the patient and their families, and help the NHS to continue to deliver high quality care with constrained resources. Given the potential importance of this work to the health and social care system, IFS are strongly committed to reaching out to policymakers to disseminate the findings. To do so, IFS will produce written work targeted at policymakers, including a non-technical report describing their methodology and their findings. This will be freely available on the Institute for Fiscal Studies website. IFS will also provide support in communicating the results, including through a press release; a brief observation note highlighting key findings; and a launch event which will be open to policymakers and the media. This will take place in early 2017 (the end of the project and release of the report). In addition, IFS plan to reach out directly to key policymakers within the health and social care system. IFS will approach strategists at NHS England and Monitor to discuss the results of their cost-benefit analysis of early intervention. This will give them rigorously-researched information on the effectiveness of early interventions, which they can use to inform and support future strategies. IFS also plan to communicate with health policy organisations, such as the King’s Fund. In addition, this work will have wider impacts beyond the health and social care system. IFS are already working closely with policymakers within the Department for Education as well as practitioners from the early childhood development field. IFS have the strong support of this advisory group in maximising their policy impact, including their assistance in disseminating the results widely through their networks. Finally, IFS have existing links with politicians from the all-party parliamentary group 1001 Critical Days and the Foundation Years Information and Research group. These groups have already expressed interest in the results. Once the report is published in March 2017, IFS will meet with them to discuss the results and the policy implications of their findings. The findings will inform the recommendations of these groups and will provide a stronger evidence base for early intervention in the UK and ensure value for money is delivered. (8) Quantifying the market size for cannabis is important given vigorous policy debates about how to intervene in this market. A body of evidence across disciplines has established significant private and social costs associated with the market for cannabis. Private costs borne by users include longer-term impacts on health from prolonged and heavy use [Fergusson and Horwood 1997, Hall and Degenhardt 2009, Marshall et al. 2011], as well as a potentially increased propensity to use other illicit substances [van Ours 2003, Kelly and Rasul 2014]. The social costs of the cannabis market arising through the health systems are substantial. For example, Public Health England estimates that drug misuse costs the NHS in England £488m annually (Public Health England 2013). Better understanding of the market size of cannabis can therefore bring measurable benefits to health and social care. The statistics based on the HES allow to compare cannabis market size estimates (by TV region equivalent and time period) based on sales data from tobacco-related products to hospital admissions for related diagnoses, and will assist policy makers by providing better understanding of how these measures are related. In particular, better understanding of the market size for cannabis can inform policy makers in the area of health and social care by providing important information about the size of the cannabis market, including as input into cost-benefit analysis (CBA). NHS Health Scotland has made extensive use of alcohol sales data to monitor and evaluate Scotland’s alcohol strategy (NHS Health Scotland 2016), and concluded that sales data can contribute to ensuring that “consumption or related harm is spotted early”, and identified as an area for future research the relationship between “consumption [of alcohol] and harm within Scotland and the rest of the UK” (NHS Health Scotland 2016). The proposed project provides evidence on such a question in the context of drug use, and will assist policy-makers by providing insight into whether such an approach could be used for illicit substances such as cannabis, and develops a new method for doing so. IFS will write a policy-focused summary of the results aimed at a practitioner’s audience and the general public, using widely read dissemination websites such as “VoxEU” (http://voxeu.org/content/topics/health-economics) or “The Conversation” (http://theconversation.com/uk/health). For example, previous policy-relevant research summaries by the researchers on VoxEU have been accessed more than 20,000 times. Links with a prominent professor (University of Essex), who has worked and advised widely on topics related to health and risky behaviours, will also help to disseminate findings to a policy-maker audience. IFS will distribute the findings to their links and contacts in the NHS. The research paper will be available freely on their website. These benefits will accrue of the next three years as the paper is prepared, as well as subsequently when the results are available in the publication. ADDITIONAL CITATIONS: NHS Health Scotland (2016): “Monitoring and Evaluating Scotland’s Alcohol Strategy,” Final Annual Report, March 2016. Public Health England (2013): “Alcohol and drugs prevention, treatment and recovery: why invest?,” PHE publications gateway number: 2013-190. (9) Consultants play a crucial role in the delivery of NHS healthcare. However, little is understood about the extent to which patient outcomes depend on the individual consultant who is responsible for their care. This project will provide evidence on the distribution of consultant effects. IFS will meet with NHS England and the Department of Health to discuss the results of their analysis and how the model could be used to guide policy to benefit the health and social care system. The model be used for policy experiments such as “what is the impact of patient survival rates if the 5% of worst performing consultants were replaced by median-performance consultants?”. DH, NHS England or Acute Trusts may use this as a basis for deciding whether some consultants require more training, additional support from the Acute Trust, or should be moved to other positions. Similar work has been carried out in the past in the United States and these models have been widely adopted by State hospital boards to evaluate provider performance, with positive implications for patient outcomes. By 2006, 47 states used similar models to produce publicly available ‘report cards’ for hospitals. Economic evaluations of the adoption of these hospital performance measures have indicated substantial improvements in the quality of care received by patients at previously poorly-performing providers. For example, researchers found a reduction of a third in the mortality rate of patients undergoing coronary artery bypass graft surgery in New York State between 1991 and 1997 in hospitals which had received a ‘high-mortality’ flag in the previous year (Cutler, Huckman and Landrum, 2004). Similarly, surgeon-specific report cards in Pennsylvania led to significant improvements in risk-adjusted mortality rates in the following years (Kolstad, 2013). IFS researchers have also met with a number of policymakers to discuss a number of projects covered by this agreement. In December 2016, they met with representatives from the Competition and Markets Authority (Chief Economist) to discuss findings from projects 3,4 and 9. These discussions will inform CMA analysis relating to hospital mergers (on the consequences for technology, hospital competition and the NHS workforce). In May 2017, IFS researchers met with representatives of Public Health England (Chief Economist) to discuss work on projects 3, 4 and 5. IFS researchers also met with representatives from the Department of Health in May 2017 (Chief Economist) to discuss IFS work on health care (specifically projects 1,4,6 and 9). These discussions will help to inform future PHE and DH internal analysis on provision of NHS health care, and IFS will provide further updates to these policy makers when the results are finalised. From May2017 (10) This analysis will improve knowledge of how the four-hour target influences patient treatments and outcomes. This will enable policymakers (in particular, NHS Improvement and the Department of Health) to evaluate whether they want to continue to enforce this target, and to choose which target level they wish to implement, by clearly setting out whether the potential benefits of a stricter policy target (through reduced waiting times) are outweighed by increased distortions to treatment decisions (through excessive or insufficient admissions) and/or unfavourable patient outcomes. This is particularly important given the recent announcement (July 2016) that 53 trusts are temporarily exempt from the target. The planned work has already been discussed with NHS Improvement’s Emergency Care Improvement team. IFS will hold further discussions with NHS Improvement to ensure that the findings are presented in a way that enables practical implementation. Benefits to commissioning (and eventually to patient care), will therefore be achieved through increasing NHS Improvement’s knowledge of the effects of the target on patient health and hospital decision-making. (11) The 2000s saw a radical shift in policy which mandated choice for patients. This choice was enabled by changing the method of paying hospitals to activity based funding (HRGs) and encouraging the entry of private providers into the provision of services, particularly elective care for joint replacement and cataract surgery. In addition, the policy was accompanied by a large increase in the NHS budget. The short terms impacts of these policies have been evaluated (the primarily focus has been on the period 2003-2009). But this radical change has had dynamic consequences. In addition, the financial landscape has altered to one of fiscal austerity for the NHS. IFS are now in a position to evaluate (a) whether the beneficial initial effects that were seen have had longer lasting effects and (b) the impact of running this policy in a period of fiscal austerity compared to one of fiscal generosity. Establishing at system level whether the policy continues to bring benefits is extremely important for the design of future changes to the NHS and social care system. Essentially IFS need to know whether this policy has been able to maintain its initial gains or whether the policy can only deliver gains when there is spare capacity. This is very important at a macro-system level. Radical change is expensive and the burden is borne by the tax payer. IFS need to be able to establish whether this type of radical change has net costs for the healthcare system as a whole. The knowledge produced by this work would enable national commissioners and policymakers to improve commissioning by better understanding the various impacts of wide-spread choice and competition policy. (12) The NICE Clinical Guideline on Routine Postnatal Care (CG37) recommends that “All healthcare providers (hospitals and community) should implement an externally evaluated structured programme that encourages breastfeeding, using the Baby Friendly Initiative (BFI) as a minimum standard.” However, the following “Further research to evaluate the effectiveness of BFI compared to another programme, or to standard care, should be carried out.” Moreover, in a recent report commissioned by UNICEF (Renfrew et al 2012), it is clear that the strength of the evidence on the effect of breastfeeding on some health outcomes is still weak (mother: Ovarian cancer and Type 2 diabetes, child: Asthma, diabetes, leukaemia, coeliac disease, cardiovascular disease, and sepsis in the child.) The study will provide evidence on the health benefits of breastfeeding (what diseases will decreases if breastfeeding rates improve). This knowledge will benefit commissioners because it will provide them with evidence that they can use to decide whether improving breastfeeding support services (such as BFI) will improve health and reduce future health care costs. It will also benefit NICE which has called for more research on the effectiveness of the breastfeeding support services in its guidelines. Health care providers, clinicians, as well as UNICEF will also benefit because they will learn whether the BFI initiative is effective or whether improvements are necessary. IFS will disseminate their research through press releases, a IFS observation (non-technical document), seminars, and a peer reviewed journal article that is read by clinicians. IFS are in direct contact with UNICEF and will communicate directly the findings to them. UNICEF are in direct contact with UK health care providers, and changes to the delivery of natal care can be delivered through this channel. IFS will communicate the findings to NICE to improve the evidence base on the benefits of breastfeeding when they next update their guideline on routine post-natal care. (13) Research spending in the UK on health is around £8 billion annually, with £3.3bn of that being publicly funded research carried out in the university and not-for-profit sectors. However, very little is known about the productivity of this investment, and what are the determinants of innovation take-up. In response to this, the Economic and Social Research Council (ESRC) joined forces with the National Institute of Health and issued a funding call to fill this gap. The network was awarded to the IFS in the UK and the NBER in the US (http://gtr.rcuk.ac.uk/projects?ref=ES/M008673/1) in October 2014. The research specified here will allow responding to this need identified by the ESRC by studying the take-up of medical innovations. The study will provide evidence on whether less research intensive hospitals are slower at adopting medical innovations, and the cost in terms of worse health outcomes that it causes. This will provide evidence to the Department of Health and NHS England on the benefits of recommending and adopting new technologies. This will enable national policymakers and commissioners to encourage practioners to quickly adopt new treatments, improving patient health and reducing health inequalities. This knowledge will be communicated to policymakers through the existing network of IFS contacts within these organisations, and through press-released non-technical reports. IFS will also improve the knowledge base for commissioners of medical research (such as the Medical Research Council and Economic and Social Research Council), which will aid them to fund high-value research. IFS will communicate directly with contacts at the ESRC (as part of the network funding this research and wider existing contacts). (14) This work will increase knowledge of the effects of breastfeeding on child health, particularly for low socio-economic status patients. This knowledge will enable national commissioners (particularly NICE) to make clearer recommendations for breastfeeding practices. This will benefit the system by improving child health and cognitive development, and potentially reducing future healthcare and education costs. IFS will communicate this knowledge to commissioners and practitioners through press-releasing the research findings, and seeking meetings with contacts at NICE. Project (12) informs policy makers about the benefits of the UNICEF Baby Friendly Initiative, whilst project (14) informs policy makers about the benefits of breastfeeding. Moreover, project (12) informs on the benefits measured using re-admission, A&E visits, and presentation of certain medical conditions; whilst project (14) informs on the benefits measured using children¹s cognitive development as well as health measures which are independent of health care use (anthropometric measures). (15) The aim is to inform and influence policy making relating to emergency admissions in England in a way that improves patient care. By demonstrating how emergency admissions can negatively affect patient care, and evaluating the role of past and future policies on this, the research will enable policymakers (e.g. Department of Health, NHS Improvement) to consider options that mitigate the effects of emergency admissions on patient care. This would provide knowledge and evidence that speaks directly to immediate policy concerns about shortages of capacity in the NHS and progress has already begun in engaging policymakers on this issue (see existing outputs in the "outputs" section, purpose 15). References Cutler, D, R. Huckman and M. Landrum. (2004). The role of information in medical markets: An analysis of publicly reported outcomes in cardiac surgery.American Economic Review 94 (2): 342-346 Fergusson, D.M. and l.J. Horwood (1997) “Early Onset of Cannabis Use and Psycho-social Adjustment in Young Adults,” Addiction 92: 279-96. Hall, W and l. Degenhardt (2009) “Adverse Health Effects of Non-medical Cannabis Use,” Lancet 374: 1383-91. Kelly, E. and I. Rasul (2014) “Policing Cannabis and Drug Related Hospital Admissions: Evidence from Administrative Records,” Journal of Public Economics 112: 89-114. Kolstad, J., (2013). Information and Quality When Motivation is Intrinsic: Evidence from Surgeon Report Cards, American Economic Review 2013, 103(7): 2875–2910 Marshall, K.S., l. Gowing and R. Ali (2011) “Pharmacotherapies for Cannabis Withdrawal,” Cochrane Database for Systematic Reviews 1: CD008940. National Institute of Clinical Excellent. Routine Postnatal Care of Women and Their Babies. NICE Clinical Guideline 37. July 2006. Renfrew MJ, Pokhrel S, Quigley M, et al. Preventing Disease and Saving Resources: The Potential Contribution of Increasing Breastfeeding Rates in the UK. London, UK: UNICEF UK2012. Van Ours, J. (2003) “Is Cannabis a Stepping-stone for Cocaine?,” Journal of Health Economics 22: 539-54.
THE NUFFIELD TRUST	THE NUFFIELD TRUST	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The Nuffield Trust is an independent research group overseen by a board of Trustees including a number of senior NHS clinicians, managers and academics. The Nuffield Trust undertakes work for the public good and within a research governance framework. The purposes for receiving HES data falls into the following categories :- 1. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The projects are: • Evaluation of the Integrated Care ‘Pioneers’. These are models of care aimed at reducing the impact of boundaries between care providers. This work is in partnership with the DH Policy Innovation Research Unit based at the London School of Hygiene and Tropical Medicine. • Evaluation of new models of primary care. This is an evaluation of selected ‘scaled up’ GP practices, to explore development of new general practice organisations. • Evaluation of a local scheme to deliver improved care for complex cases – as part of the PM’s Challenge Fund in Barking, Havering and Redbridge (funded by PM’s Challenge Fund). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In these studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The projects are: • A DH funded project to look at ways to identify people who suffered avoidable serious harm. This will test whether HES data can be used as a screening tool to identify cases for specific audit. This work will be undertaken in conjunction with the London School of Hygiene and Tropical Medicine and Imperial College London. • Patterns of urgent care use related to the development of ambulatory emergency medicine, a new approach being adopted in hospitals around the country. The Nuffield Trust will look at the impact on patterns of acute hospital use and long terms outcomes for patients. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). • An assessment of the impact of alcohol on hospital services. This study aims to evaluate whether alcohol is an increasing burden on acute hospital services, will attempt to identify geographical areas where hospital alcohol teams are working well and aims to share opportunities identified for improvement. UPDATED INFORMATION OCT 2016 : This project is now closed and no longer requires the processing data for this use (apart from auditing purposes). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. The projects: • Descriptions of differential patterns of hospital use (admissions, lengths of stay, child health indicators) by area and provider. This work aims to identify good practice in the efficient use of hospital resources. 4. Linkage of HES data to linked datasets provided by NHS Digital A number of other projects require access to HES data, but also make use of a linked dataset such as HES-ONS. These are subject to separate applications to NHS Digital, but would not require a separate data release. Instead the applicant would make use of the data provided under this agreement. Such projects typically cover 10 years of HES data. 5. Linkage of HES data to HES ids provided by NHS Digital regarding a specific cohort. Such uses would again be subject to separate data agreements, since they would have a different legal basis and potentially involve patient consent. They would be considered separately under different applications but would not require a separate data release. 6. Informing the public debate about hospital use The Nuffield Trust regularly acts to improve the quality of public debate on use of hospital services by publishing responsive research, which helps focus the debate on evidence. Trigger for this work include a specific issue suddenly coming to national prominence, or an individual or organisation making an assertion which is easily tested using data already available. As an independent research organisation and registered charity, with independence from party politics overseen by the board of trustees, such interventions are carefully considered to ensure that an evidence-based statement may add value to the overall debate. They are not provided at the request of any individual organisation. 7. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The project involves an evaluation of Virtual Wards in Devon. This study aims to evaluate a multidisciplinary care management scheme which was delivered to individuals in their homes. This work is intended to follow up an earlier study funded by NIHR which was only able to capture the first hundred patients admitted to the Virtual Ward. Over subsequent years, six thousand individuals have been admitted to the scheme. These were chosen as they were judged to have a high risk of hospitalisation. Nuffield will test whether the post-Virtual Ward hospital admissions were low within this group, compared to a similar cohort of people from other parts of the country selected from HES. UPDATED INFORMATION OCT 2016: This project is now closed and no longer requires processing of the data for this use (apart from auditing purposes). 8. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The project involves evaluating the implementation of Quality Standards in the London region, which were developed and introduced to reduce variability in and improve patient care. The study aims to: - investigate variation in outcomes and adherence to standards across London - determine if there is an association between degrees of standard implementation and outcomes - evaluate innovative outcome measures to investigate the impact of standards on patient care and for monitoring standard adherence UPDATED ADDITIONAL PROJECT information OCT 16 The following describes new projects the Nuffield Trust are requesting access to HES data for; 9. Research studies identifying models of medical generalism used in smaller hospitals and exploring their strengths and weaknesses in treating older and more complex patients from patient, professional and service perspectives. The rising numbers of older and more complex patients is considered to be one of the most pressing problems facing the NHS. Although they receive the most resource-intensive care, their problems are less likely to be accurately diagnosed and have more adverse outcomes than other age groups. The emerging consensus is that current models of hospital care, which are heavily based around specialists delivering disease-specific care, serve these patients poorly, as it is often fragmented and poorly co-ordinated. A revival of medical generalism has been suggested to provide better and more cost-effective care. The reality, however, is that there is a paucity of evidence on which to base new models of medical generalism. Smaller hospitals provide an ideal environment in which to investigate models of medical generalist care, as their patient population is older and more vulnerable, while their size creates constraints on their income, capacity and staffing. The overarching aim of this research, therefore, is to identify the models of medical generalism used in smaller hospitals and explore their strengths and weaknesses from patient, professional and service perspectives. More specifically, Nuffield Trust will be using HES data to create a classification of patients that might benefit from general medical care and, based on this classification, provide a descriptive analysis of the workloads of smaller hospitals. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data The number of unplanned emergency readmissions to hospital have often been cited as a marker of quality of hospital care. Indeed, in England, readmissions have been used to influence hospital reimbursements. A number of studies, however, have shown that readmissions are complex and can be linked with a range of factors other than preventable or avoidable harms. If the quality of care at hospital level and individual patient characteristics are not the sole drivers of readmission then the additional factors must lie in the way different health systems manage patients. One way to consider the impact of systemic differences in health systems is to use international comparisons as a form of a natural experiment to see if patterns of readmission are similar or different. Though such comparisons cannot definitely identify the reasons behind differences, they can prompt useful questions on the effects of different health systems. In this analysis Nuffield Trust want to test whether two different health systems demonstrate a fundamentally different pattern of hospital readmissions. Within any health system there are variations in readmissions rates between areas - the product of a host of patient and health system level factors influencing decisions and resource use. In order to understand the impacts of the broader health systems then we need to consider the overall distributions of readmissions and standardise – as far as possible – for differences at the patient level attributable to the underlying health problems. The aim will be to analyse HES data covering admissions to NHS hospitals for selected years and calculate overall readmission rates. In parallel we will calculate the equivalent readmission metrics using the Dutch national data for the same time period. Nuffield Trust will then test for statistically significant differences in readmission rates between the two countries, and quantify to what extent any differences can be explained by patient-level factors (e.g. age, deprivation, severity) and the extent to which any variation could be explained by differences in the two health systems. Nuffield Trust will also attempt to distinguish between potentially preventable readmissions and other reasons for readmissions, using the administrative data, and produce a comparison between the two systems.	The data requested and already disseminated will be accessed and processed by substantive employees of The Nuffield Trust and only for the purposes described in the application. Whilst the nature of detailed analysis in relation to each project varies, the broad context of processing is consistent. In summary :- - The data is downloaded from NHS Digital and imported into SAS. The server is held on-site, and access is restricted to named individuals according to The Nuffield Trust’s security policy. - The data is held within separate folders within the server. - Remote access to the database is permitted, but only through Citrix via secure token (so processing is still carried out on site), and with local printing and downloading disabled. - Only staff who have signed a confidentiality agreement and have received IG training are permitted access. - All access to individual files is recorded, and a sample audited to investigate the existence of any adverse incidents, and ensure that appropriate access has been maintained. - Once held in SAS, the researcher will view the data and select a specific cohort for each individual study. Commonly a process will initially take place to define the particular cohort of interest in terms of e.g. individual diagnostic codes or procedure codes. The researchers will use routinely available filter definitions where possible, but may amend these based on the nature of each study’s group of interest. Depending on the research a similar control group may be established. - The individual researcher then analyses the data, before applying the relevant disclosure controls to any output. Software used will be SAS, R and stata; typically this will involve analysis on several outcome measures, risk adjustment and the construction of control groups. - No record level data would be linked to this dataset, but it may be combined with publically available demographic or geographic data, for example in relation to local Trust performance - Outputs are thus produced which consist of aggregate data (or indicator/statistical data) only. As an example, for the assessment of the burden of alcohol on hospital services, The Nuffield Trust will look at national trends of alcohol related A&E attendances and inpatient admissions for alcohol related liver disease (ARLD) over the most recent decade. The Nuffield Trust will also identify a cohort of patients diagnosed with ARLD for the first time, and examine their prior and post diagnosis patterns of hospital use, in the context of a comparator group. Analyses will be undertaken at local authority level and will take into account provision of acute trust alcohol services. In all such work, The Nuffield Trust analyse patterns of hospital activity by area, by year, by condition or by provider, developing comparative analyses and standardising for a range of episode level, or patient level variables – such as age, the presence of a long terms condition, prior patterns of use. The analyses commonly follow the health and care of a well-defined cohort of individuals over a lengthy period of time (for example the alcohol study will follow for ten years, the child health study – part of the differential patterns of health – will evaluate a cohort throughout childhood). Such analyses require complex processing for fair comparisons and to capture activity for whole populations – something that only nationally collated data can provide. The datasets necessary for each of the studies are listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. *Study will require further years of data beyond those being requested in this application. : - Integrated Care Models (Pioneers). • APC, OP, AE 2004/05 to 2019/20* - New models of primary care • APC, OP, AE 2003/04 to 2015/16 - PM’s challenge fund in Barking, Havering and Redbridge • APC, OP, AE 2004/05 to 2015/16 - Avoidable harms project • APC, OP, AE 2003/04 to 2016/17* - Ambulatory emergency care project. (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2015/16 - Assessment of the burden of alcohol on hospital services (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2013/14 - Descriptions of differential patterns of hospital use by area and provider. • APC, OP, AE 1997/98 to 2015/16 For the study of Virtual Wards in Devon, the Nuffield Trust will use HES data from a small set of areas chosen as being similar to Devon (these areas are Somerset, Cornwall, Shropshire and Herefordshire). From these areas, the researchers will select a pseudo control group of individuals who shared characteristics with individuals who were admitted to the Virtual Ward scheme in Devon. These characteristics include age, sex, prior use of hospital services, and diagnostic history. The characteristics and hospital utilisation of the individuals admitted to Virtual Wards will be derived using a locally sourced pseudonymised data set (SUS). We will then compare future unplanned and other admissions to test for differences between the Devon Virtual Wards cohort and the pseudo control group. For the evaluation of the implementation of Quality Standards in the London region The Nuffield Trust will look at variation in outcomes by London hospital/trust by carrying out cross section analysis at different time points, analysing the changes in extent of variation over time, investigating variations in trends and looking at these in different patient groups. The datasets necessary for the new study is listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. - Evaluation of Virtual Wards in Devon. (UPDATED OCT 2016: This project is now closed and no longer require processing data for this use (apart from auditing purposes). • APC, OP, AE 2006/07 to 2014/15 - Evaluating the implementation of Quality Standards in the London region • APC, OP, AE 2005/06 to 2015/16 UPDATED ADDITIONAL PROJECT information OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. - Research studies identifying and evaluating models of medical generalism • APC, OP, AE 2010/11 to 2015/16 - Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data • APC 2013/14 to 2015/16 Additional note – third parties The Nuffield Trust will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuffield Trust for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide.	Anticipated dates of study reports are listed. All may also include presentational web material (for example slideshows and blog posts), in addition to presentations given in person at relevant research or policy conferences, etc. 1. Evaluations of the impact of innovations in health and social care on hospital utilisation • - Integrated Care Models (Pioneers).Interim report in 2016, with annual reports from 2016 to 2020 (final report in 2020). - New models of primary care • Interim report, September 2015. Final Nuffield Trust research reports will be available Autumn 2016. Update April 2016: Nuffield Trust publication due in June 2016 and planning a subsequent separate academic research paper - PM’s challenge fund in Barking, Havering and Redbridge • Interim report to local areas, June 2015. Final Nuffield Trust research report, peer review articles - summer 2016. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. - Avoidable harm project • Reports co published with LSHTM. Final report to NIHR available January 2017. Also peer review articles at this time. - Ambulatory emergency care project • Nuffield Trust preliminary report (evaluation of pilot sites) – spring 2015 and if successful we will agree a more comprehensive longer term evaluation proposal. UPDATED OCT 2016: This project is now closed andno longer require processing data for this use (apart from auditing purposes). - Assessment of the burden of alcohol on hospital services • Nuffield Trust report completed by summer 2015, with peer reviewed papers to be submitted also in summer 2015. UPDATED OCT 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. - Descriptions of differential patterns of hospital use by area and provider. • Analysis of length of stay led to workshop in September 2014 (with Monitor), research report planned for winter 2014. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). Child health study, to report 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual outputs will be covered within separate applications 5. Linkage of HES data to HES ids provided by HSCIC regarding a specific cohort Individual outputs will be covered within separate applications 6. Informing the public debate about hospital use The outputs of this work will be in the form of blogs, briefings, and/or presentation slideshows, posted on the Nuffield Trust website and made freely available to all. Due the responsive nature of the work, The Nuffield Trust are not able to provide prospective dates for these outputs 7. Evaluation of Virtual Wards in Devon Nuffield trust report to be published by end of 2015. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 8. Evaluating the implementation of Quality Standards in the London region • Nuffield Trust report to be published by mid-2016 The report will require quantitative analysis, the methods for which will be piloted in a Masters dissertation to be completed by the end of 2015. This work, which is overseen by a senior analyst, will lead to a peer reviewed publication to be submitted also in early 2016. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Outputs in late 2017 will include a final report, an executive summary, and summary results for a lay audience. All will be made publically available on the Nuffield Trust website. Other planned mechanisms for dissemination include: the packaging and provision of on-going feedback to participating hospitals; workshops with user groups; face to face engagement with policy makers at national level; explicit knowledge transfer and exchange initiatives, such as working with networks such as the NHS Confederation. All data published/disseminated will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines. At the same time abstracts will be submitted to key conferences, such as Future Hospital Programme of the RCPL, Quality and Safety in Health Care Forum, the NHS Confederation Conference, as well as NIHR events. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Summary report published on Nuffield Trust website in winter 2016 and with an aim to publish in a reputable, peer reviewed journal by early 2017. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. All data published will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines.	Over the past five years The Nuffield Trust’s research studies, using NHS data, have been widely used to inform decision making and debate in health care. The Nuffield Trust publishes their reports on the Nuffield website and in peer reviewed journals where appropriate. The Nuffield Trust list below recent reports of studies where they have made use of HES data; There are many examples of The Nuffield Trust’s work being cited in parliamentary debates and select committees as well as used by national bodies including the Department of Health and NHS England, CQC and Monitor. Many of the projects have been funded by the Department of Health and NHS, and The Nuffield Trust work in partnership with NHS and other care organisations and with universities. The Nuffield Trust has also provided examples of their studies for NHS Digital to use as evidence to the health select committee. The benefits of The Nuffield Trust’s work are seen in terms of decisions made by healthcare commissioners and providers, when thinking about the types of services needed to deliver benefits to patients, as well as by policy makers. The following provides benefits for each of the projects; 1. Evaluations of the impact of innovations in health and social care on hospital utilisation. The study of Integrated Care Models will inform health care providers, commissioners and policymakers of the impacts of new forms of integrated care emerging under the banner of national ‘Pioneer projects’. This is a piece of applied research funded by DH and with a very wide audience. Throughout the study, the Nuffield Trust will be engaging with the Pioneer sites themselves and with others interested in developing new models of care. This aligns with national policy goals to provide a better care experiences through integration of services. Over the past few years a range of new forms of GP organisations have emerged. These are seen as one solution to systemic problems facing primary care; however there is little empirical analysis of these new organisational forms. The Nuffield Trust’s study of new models of primary care will be looking at the impact of networks of practices in terms of the way they have changed patient care and service delivery. Such analysis will be critical to future NHS planning on how to organise primary care services. The results will be of key interest all those involved in general practice, primary care and the commissioning of out of hospital services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented preliminary results to members of the New Models of Primary Care Network at quarterly meetings. Improving access to primary has been a matter of concern at the highest levels of government. In 2013 the PM created a Challenge Fund to look at new models of care. As evaluation partners of Barking, Havering and Redbridge’s successful bid to adapt services in the local area, The Nuffield Trust’s work will give provide a judgement about how successful these schemes might be. This will directly impact on local commissioners, and have implications across the country. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In all such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The Nuffield Trust’s work on serious avoidable harms has the possibility of contributing to safer care and better methods for future monitoring systems. Ambulatory emergency care (AEC) provides a model of care for patients who have urgent care needs, but do not necessarily warrant an acute hospital admission. Though a number of local evaluative studies have been undertaken there are no systematic analyses across a range of organisations providing AEC. Through analysing the impacts of these schemes we can identify which types are most successful in delivering better care for patients. These can serve as models of success for areas wishing to develop their own services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented findings at British Association of Ambulatory Emergency Care Annual National Conference 2015. This project is now closed andno longer requires processing data for this use (apart from auditing purposes). The Nuffield Trust’s study of alcohol aims to understand the burden on hospital services and to identify areas where preventative action is working well. This work will consider effectiveness of different models of service provision which will meet a specific recommendation for future work made by Public Health England following their survey of hospital alcohol teams. The findings will be of interest to Health and Wellbeing Boards and to local commissioners of alcohol services and aims to share learning of how the burden of alcohol to hospitals can be reduced. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has – (1) Produced a paper "Alcohol-specific activity in hospitals in England" published on 22nd Dec 2015, along with blog "The sobering burden of alcohol on the NHS". (2) Submitted an academic paper to the BMC Public Health "The impact of alcohol care teams on emergency secondary care use following a diagnosis of alcoholic liver disease - a national difference-in-difference study." on 12/1/2016 . (3) Public Health England Conference abstract poster presentation "Hospital use before and after first recorded diagnosis of alcohol related liver disease in England: Opportunities for early intervention to reduce harm". This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. These pieces of work aim to identify good practice in the efficient use of hospital resources. The Nuffield Trust’s future modelling will be used (as their past modelling currently is) to inform public debate about health service provision and central planning assumptions about future needs. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has, through the QualityWatch (http://www.qualitywatch.org.uk/) programme, produced a series of reports, carried out several activities and had the following impact. This includes – 1. Hospital admissions from care homes (29th January 2015). Subsequent to publication this was referenced in the evidence for NICE guidance: Transition between inpatient hospital settings and community or care home settings for adults with social care needs (November 2015) 2. Mental ill health and hospital use (14th October 2015). Nuffield Trust has presented the findings at the Public Health England Conference (September 2015) and at the International Conference of Integrated care (May 2016). Nuffield Trust has also had several contacts with local healthcare providers for guidance on how to apply the methods to carry out the same analysis locally. Whilst it is hard to draw causality, Nuffield have also been made aware of NHS England undertaking work to drive improvements in the quality of physical health care provided by mental health providers to service users with severe and enduring mental ill health and will be developing a national clinical audit to underpin this under the National Mental Health CQUIN scheme for 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual benefits will be covered within separate applications 5. Linkage of HES data to HES IDs provided by HSCIC regarding a specific cohort Individual benefits will be covered within separate applications 6. Informing the public debate about hospital use The Nuffield Trust’s responsive analyses will improve the quality of public debate on hospital use by broadening the available evidence base, focusing the debate on evidence rather than assertion and potentially preventing poor policy decisions. This work is widely reported in the media and Nuffield regularly meet with senior policy-makers and leaders in the NHS to discuss thiswork. 7. Evaluation of Virtual Wards in Devon The evaluation of Virtual Wards fits within a suite of work the Nuffield are undertaking on community based alternatives to hospital care. An earlier study of ours (funded by NIHR) included a very early assessment of Devon’s virtual wards [1], but the number of people recruited was too small to make any robust assessment of whether the scheme had reduced future unplanned admissions. In the following years 6,000 primarily older people have been provided with care in a home based virtual ward. This cohort size should give us the power to detect significant differences, even where these are relatively small. The overall aim is to provide evidence to the health service on methods of care which help people to stay independent of hospital for longer. [1] Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes) 8. Evaluating the implementation of Quality Standards in the London region This work aims to assess the extent of the patient benefits delivered by the introduction of new quality standards and to evaluate a new set of outcome indicators that can be used to more accurately measure their benefits. Applying these standards has a potential direct impact on the quality of over a million care episodes a year – over 500,000 patients. Moreover the London standards will be incorporated into the national Keogh standards. As a consequence this work on standards and outcomes will be of relevance to every hospital in the NHS with prospects of being incorporated into national audit tools. Nuffield will be presenting the outcomes in peer-reviewed reports and using Nuffield's extensive network of contacts to target these towards key decision makers. This will be facilitated by the Trust’s communications team who are very experienced with this type of activity. There is also direct interest from NHS London who have expressed an interest in funding further pieces of work in this area. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Three separate panels of experts, the Independent Commission for the Royal College of General Practitioners and the Health Foundation, The Royal College of Physicians of London’s (RCPL) Future Hospital Commission (FHC) and the General Medical Council’s Shape of Training review, have all recommended a revival in general medicine to better provide high-quality, cost-effective care. However, general medicine cannot be rapidly reintroduced, nor hospitals, let alone smaller ones, reconfigure services unless there is a clear understanding of patient need and how different models of current medical care meet these. With NHS England’s Viable Smaller Hospitals workstream of the New Models of Care programme already underway, there is an urgent need for clear and comprehensive evidence to guide future policy and service reconfiguration. This study has the potential to have a major impact at the national and international levels, as the debate so far around medical generalism has operated largely at an abstract level. This research will provide much needed evidence to ground the debate in the empirical and experiential realities of underlying patient need. It will provide a theoretically informed evidence base from which to take the debate forward. This will not only have relevance for unscheduled adult medical care, but could also be applied to other clinical areas, such as surgery and paediatrics. It also has relevance to larger hospitals, as well as informing much broader, international debates around matching medical workforce to growing patient need. This research should influence decision making around: • Ways of working in hospitals • The education of doctors at undergraduate and graduate level • The number and types of doctors required in the UK • Continuing professional development for physicians • Legislative and contractual arrangements for doctors • The future of smaller hospitals and their role in the wider healthcare system 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Despite the prominence of readmission rates in discourse around health service organisation, there are few empirical studies that compare the rates of readmission across different health systems. This analysis will provide a thorough comparison between readmission rates in England and the Netherlands, and also allow a deeper understanding of which readmissions are truly preventable and those that occur for other reasons. Readmissions due to complications are a burden for patients and account for high healthcare costs: this research will provide insight into the types of readmissions that should really be included in hospital-level indicators to get a valid representation of quality improvement potential. By way of further evidence as to the use made of HES, a non-exhaustive list of recent publication using HES data is provided below. All such articles are in the public domain, and many relate directly to current health practice or topics of interest, and have been commissioned by the NHS or the Department of Health. Nuffield Trust Research Group - Recent publications that have used HES (October 2014) • Holly Dorning and Martin Bardsley. Focus On Allied Health Professionals. Nuffield Trust September 2014. • Theo Georghiou and Martin Bardsley. Exploring the costs of end of life care. Nuffield Trust September 2014. • Ian Blunt, Martin Bardsley & Giovanni FM Strippoli. Predialysis hospital use and late referrals in incident dialysis patients in England: a retrospective cohort study. Nephrology Dialysis Transplantation. Nephrol. Dial. Transplant. (2014) doi: 10.1093/ndt/gfu284 • Steventon A, Bardsley M, Doll H, Tuckey E, Newman S. Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. Submitted for publication. BMC Health Services Research. June 2014 • Steventon A, Bardsley M and Mays N. Effect of a telephonic alert system (Healthy Outlook) for patients with chronic obstructive pulmonary disease: cohort study with matched controls. Journal of Public Health Advance Access published July 10, 2014. pp. 1–9 doi:10.1093/pubmed/fdu042 • Blunt I, Bardsley M, Grove A, et al. Classifying emergency 30-day readmissions in England using routine hospital data 2004–2010: what is the scope for reduction? Emerg Med J Published Online First 26/3/14 doi:10.1136/emermed-2013-202531 • Lewis, G. H., Vaithianathan, R., Wright, L., Brice, M. R., Lovell, P., Rankin, S., & Bardsley, M. (2013, November 4). Integrating care for high-risk patients in England using the virtual ward model: lessons in the process of care integration from three case sites. International Journal of Integrated Care. Retrieved from http://www.ijic.org/index.php/ijic/article/view/URN%3ANBN%3ANL%3AUI%3A10-1-114754/2197 • Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. • Steventon A, Tunkel S, Blunt I and Bardsley M. Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls. BMJ. 2013 Aug 6;347:f4585. doi: 10.1136/bmj.f4585. • Bardsley M, Doll H and Steventon A. Impact of telehealth on general practice contacts: findings from the whole systems demonstrator cluster randomised trial BMC Health Services Research 2013, 13:395 doi:10.1186/1472-6963-13-395 • Roberts A, Blunt I, Bardsley M. Focus On: Distance from home for emergency care. QualityWatch Report May 2014. Nuffield Trust/Health Foundation • Georghiou T, Cooke M, & Bardsley M. How Representative Are Patients Who Access the Marie Curie Nursing Service of the Population of People Who Die Each Year in England? BMJ Supportive & Palliative Care, 3(1), 134–134. doi:10.1136/bmjspcare-2013-000453b.27 • Natasha Curry, Matthew Harris, Laura Gunn, Yannis Pappas, Ian Blunt, Michael Soljak, Nikolaos Mastellos, Holly Holder, Judith Smith, Azeem Majeed, Agnieszka Ignatowicz, Felix Greaves, Athina Belsi, Nicola Costin-Davis, Jessica D Jones Nielsen, Geva Greenfield, Elizabeth Cecil, Susan Patterson, Josip Car, Martin Bardsley Integrated care pilot in north west London: a mixed methods evaluation. Int J Integr Care 2013; Jul–Sep, URN:NBN:NL:UI:10-1-114735 • Chitnis, X. , Georghiou, T., Steventon, A., & Bardsley, M. J. (2013). Effect of a home-based end-of-life nursing service on hospital use at the end of life and place of death: a study using administrative data and matched controls. BMJ Supportive & Palliative Care, 1–9. doi:10.1136/bmjspcare-2012-000424 • Bardsley M, Blunt I, Davies S, Dixon J. Is secondary preventive care improving? Observational study of 10 year trends in emergency admissions for conditions amenable to ambulatory care. BMJ Open 2013; :e002007. doi:10.1136/bmjopen-2012-002007 • Davies A, Chitnis X, Bardsley M. Hospital activity and cost incurred due to unregistered patients in England: considerations for current and new commissioners. J Public Health first published online December 19, 2012 doi:10.1093/pubmed/fds098 • Clarke A, Blunt I, Bardsley M. Analysis of emergency 30 day readmissions in England using routine hospital data 2004-20010.Is there scope for reduction. Presented the Society for Social Medicine Annual Scientific Meeting. Journal of Epidemiology and Community Health. September 2012 Supplement. Doi:10.1136/jech-2012-201753.117 • Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of readmission within 30 days of discharge (PARR-30). BMJ Open. 2012;00:e001667. doi:10.1136/bmjopen-2012-001667. • Roland M, Lewis R, Steventon A, Adams J, Bardsley M, Brereton L, Chitnis X, Staetsky L, Tunkel S, Ling T. Case management for at-risk elderly patients in the English Integrated Care Pilots: observational study of staff and patient experience and secondary care utilisation. International Journal of Integrated Care – Volume 12, 24 July – URN:NBN:NL:UI:10-1-113731 / ijic2012-130 – http://www.ijic.org/ • Steventon A, Bardsley M, Billings J, Dixon J, Doll H, Hirani S, Cartwright M, Rixon L, Knapp M, Henderson C, Rogers A, Fitzpatrick R, Hendy J, Newman S. Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 2012;344:e3874.
THE NUFFIELD TRUST	THE NUFFIELD TRUST	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The Nuffield Trust is an independent research group overseen by a board of Trustees including a number of senior NHS clinicians, managers and academics. The Nuffield Trust undertakes work for the public good and within a research governance framework. The purposes for receiving HES data falls into the following categories :- 1. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The projects are: • Evaluation of the Integrated Care ‘Pioneers’. These are models of care aimed at reducing the impact of boundaries between care providers. This work is in partnership with the DH Policy Innovation Research Unit based at the London School of Hygiene and Tropical Medicine. • Evaluation of new models of primary care. This is an evaluation of selected ‘scaled up’ GP practices, to explore development of new general practice organisations. • Evaluation of a local scheme to deliver improved care for complex cases – as part of the PM’s Challenge Fund in Barking, Havering and Redbridge (funded by PM’s Challenge Fund). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In these studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The projects are: • A DH funded project to look at ways to identify people who suffered avoidable serious harm. This will test whether HES data can be used as a screening tool to identify cases for specific audit. This work will be undertaken in conjunction with the London School of Hygiene and Tropical Medicine and Imperial College London. • Patterns of urgent care use related to the development of ambulatory emergency medicine, a new approach being adopted in hospitals around the country. The Nuffield Trust will look at the impact on patterns of acute hospital use and long terms outcomes for patients. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). • An assessment of the impact of alcohol on hospital services. This study aims to evaluate whether alcohol is an increasing burden on acute hospital services, will attempt to identify geographical areas where hospital alcohol teams are working well and aims to share opportunities identified for improvement. UPDATED INFORMATION OCT 2016 : This project is now closed and no longer requires the processing data for this use (apart from auditing purposes). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. The projects: • Descriptions of differential patterns of hospital use (admissions, lengths of stay, child health indicators) by area and provider. This work aims to identify good practice in the efficient use of hospital resources. 4. Linkage of HES data to linked datasets provided by NHS Digital A number of other projects require access to HES data, but also make use of a linked dataset such as HES-ONS. These are subject to separate applications to NHS Digital, but would not require a separate data release. Instead the applicant would make use of the data provided under this agreement. Such projects typically cover 10 years of HES data. 5. Linkage of HES data to HES ids provided by NHS Digital regarding a specific cohort. Such uses would again be subject to separate data agreements, since they would have a different legal basis and potentially involve patient consent. They would be considered separately under different applications but would not require a separate data release. 6. Informing the public debate about hospital use The Nuffield Trust regularly acts to improve the quality of public debate on use of hospital services by publishing responsive research, which helps focus the debate on evidence. Trigger for this work include a specific issue suddenly coming to national prominence, or an individual or organisation making an assertion which is easily tested using data already available. As an independent research organisation and registered charity, with independence from party politics overseen by the board of trustees, such interventions are carefully considered to ensure that an evidence-based statement may add value to the overall debate. They are not provided at the request of any individual organisation. 7. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The project involves an evaluation of Virtual Wards in Devon. This study aims to evaluate a multidisciplinary care management scheme which was delivered to individuals in their homes. This work is intended to follow up an earlier study funded by NIHR which was only able to capture the first hundred patients admitted to the Virtual Ward. Over subsequent years, six thousand individuals have been admitted to the scheme. These were chosen as they were judged to have a high risk of hospitalisation. Nuffield will test whether the post-Virtual Ward hospital admissions were low within this group, compared to a similar cohort of people from other parts of the country selected from HES. UPDATED INFORMATION OCT 2016: This project is now closed and no longer requires processing of the data for this use (apart from auditing purposes). 8. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The project involves evaluating the implementation of Quality Standards in the London region, which were developed and introduced to reduce variability in and improve patient care. The study aims to: - investigate variation in outcomes and adherence to standards across London - determine if there is an association between degrees of standard implementation and outcomes - evaluate innovative outcome measures to investigate the impact of standards on patient care and for monitoring standard adherence UPDATED ADDITIONAL PROJECT information OCT 16 The following describes new projects the Nuffield Trust are requesting access to HES data for; 9. Research studies identifying models of medical generalism used in smaller hospitals and exploring their strengths and weaknesses in treating older and more complex patients from patient, professional and service perspectives. The rising numbers of older and more complex patients is considered to be one of the most pressing problems facing the NHS. Although they receive the most resource-intensive care, their problems are less likely to be accurately diagnosed and have more adverse outcomes than other age groups. The emerging consensus is that current models of hospital care, which are heavily based around specialists delivering disease-specific care, serve these patients poorly, as it is often fragmented and poorly co-ordinated. A revival of medical generalism has been suggested to provide better and more cost-effective care. The reality, however, is that there is a paucity of evidence on which to base new models of medical generalism. Smaller hospitals provide an ideal environment in which to investigate models of medical generalist care, as their patient population is older and more vulnerable, while their size creates constraints on their income, capacity and staffing. The overarching aim of this research, therefore, is to identify the models of medical generalism used in smaller hospitals and explore their strengths and weaknesses from patient, professional and service perspectives. More specifically, Nuffield Trust will be using HES data to create a classification of patients that might benefit from general medical care and, based on this classification, provide a descriptive analysis of the workloads of smaller hospitals. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data The number of unplanned emergency readmissions to hospital have often been cited as a marker of quality of hospital care. Indeed, in England, readmissions have been used to influence hospital reimbursements. A number of studies, however, have shown that readmissions are complex and can be linked with a range of factors other than preventable or avoidable harms. If the quality of care at hospital level and individual patient characteristics are not the sole drivers of readmission then the additional factors must lie in the way different health systems manage patients. One way to consider the impact of systemic differences in health systems is to use international comparisons as a form of a natural experiment to see if patterns of readmission are similar or different. Though such comparisons cannot definitely identify the reasons behind differences, they can prompt useful questions on the effects of different health systems. In this analysis Nuffield Trust want to test whether two different health systems demonstrate a fundamentally different pattern of hospital readmissions. Within any health system there are variations in readmissions rates between areas - the product of a host of patient and health system level factors influencing decisions and resource use. In order to understand the impacts of the broader health systems then we need to consider the overall distributions of readmissions and standardise – as far as possible – for differences at the patient level attributable to the underlying health problems. The aim will be to analyse HES data covering admissions to NHS hospitals for selected years and calculate overall readmission rates. In parallel we will calculate the equivalent readmission metrics using the Dutch national data for the same time period. Nuffield Trust will then test for statistically significant differences in readmission rates between the two countries, and quantify to what extent any differences can be explained by patient-level factors (e.g. age, deprivation, severity) and the extent to which any variation could be explained by differences in the two health systems. Nuffield Trust will also attempt to distinguish between potentially preventable readmissions and other reasons for readmissions, using the administrative data, and produce a comparison between the two systems.	The data requested and already disseminated will be accessed and processed by substantive employees of The Nuffield Trust and only for the purposes described in the application. Whilst the nature of detailed analysis in relation to each project varies, the broad context of processing is consistent. In summary :- - The data is downloaded from NHS Digital and imported into SAS. The server is held on-site, and access is restricted to named individuals according to The Nuffield Trust’s security policy. - The data is held within separate folders within the server. - Remote access to the database is permitted, but only through Citrix via secure token (so processing is still carried out on site), and with local printing and downloading disabled. - Only staff who have signed a confidentiality agreement and have received IG training are permitted access. - All access to individual files is recorded, and a sample audited to investigate the existence of any adverse incidents, and ensure that appropriate access has been maintained. - Once held in SAS, the researcher will view the data and select a specific cohort for each individual study. Commonly a process will initially take place to define the particular cohort of interest in terms of e.g. individual diagnostic codes or procedure codes. The researchers will use routinely available filter definitions where possible, but may amend these based on the nature of each study’s group of interest. Depending on the research a similar control group may be established. - The individual researcher then analyses the data, before applying the relevant disclosure controls to any output. Software used will be SAS, R and stata; typically this will involve analysis on several outcome measures, risk adjustment and the construction of control groups. - No record level data would be linked to this dataset, but it may be combined with publically available demographic or geographic data, for example in relation to local Trust performance - Outputs are thus produced which consist of aggregate data (or indicator/statistical data) only. As an example, for the assessment of the burden of alcohol on hospital services, The Nuffield Trust will look at national trends of alcohol related A&E attendances and inpatient admissions for alcohol related liver disease (ARLD) over the most recent decade. The Nuffield Trust will also identify a cohort of patients diagnosed with ARLD for the first time, and examine their prior and post diagnosis patterns of hospital use, in the context of a comparator group. Analyses will be undertaken at local authority level and will take into account provision of acute trust alcohol services. In all such work, The Nuffield Trust analyse patterns of hospital activity by area, by year, by condition or by provider, developing comparative analyses and standardising for a range of episode level, or patient level variables – such as age, the presence of a long terms condition, prior patterns of use. The analyses commonly follow the health and care of a well-defined cohort of individuals over a lengthy period of time (for example the alcohol study will follow for ten years, the child health study – part of the differential patterns of health – will evaluate a cohort throughout childhood). Such analyses require complex processing for fair comparisons and to capture activity for whole populations – something that only nationally collated data can provide. The datasets necessary for each of the studies are listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. *Study will require further years of data beyond those being requested in this application. : - Integrated Care Models (Pioneers). • APC, OP, AE 2004/05 to 2019/20* - New models of primary care • APC, OP, AE 2003/04 to 2015/16 - PM’s challenge fund in Barking, Havering and Redbridge • APC, OP, AE 2004/05 to 2015/16 - Avoidable harms project • APC, OP, AE 2003/04 to 2016/17* - Ambulatory emergency care project. (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2015/16 - Assessment of the burden of alcohol on hospital services (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2013/14 - Descriptions of differential patterns of hospital use by area and provider. • APC, OP, AE 1997/98 to 2015/16 For the study of Virtual Wards in Devon, the Nuffield Trust will use HES data from a small set of areas chosen as being similar to Devon (these areas are Somerset, Cornwall, Shropshire and Herefordshire). From these areas, the researchers will select a pseudo control group of individuals who shared characteristics with individuals who were admitted to the Virtual Ward scheme in Devon. These characteristics include age, sex, prior use of hospital services, and diagnostic history. The characteristics and hospital utilisation of the individuals admitted to Virtual Wards will be derived using a locally sourced pseudonymised data set (SUS). We will then compare future unplanned and other admissions to test for differences between the Devon Virtual Wards cohort and the pseudo control group. For the evaluation of the implementation of Quality Standards in the London region The Nuffield Trust will look at variation in outcomes by London hospital/trust by carrying out cross section analysis at different time points, analysing the changes in extent of variation over time, investigating variations in trends and looking at these in different patient groups. The datasets necessary for the new study is listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. - Evaluation of Virtual Wards in Devon. (UPDATED OCT 2016: This project is now closed and no longer require processing data for this use (apart from auditing purposes). • APC, OP, AE 2006/07 to 2014/15 - Evaluating the implementation of Quality Standards in the London region • APC, OP, AE 2005/06 to 2015/16 UPDATED ADDITIONAL PROJECT information OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. - Research studies identifying and evaluating models of medical generalism • APC, OP, AE 2010/11 to 2015/16 - Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data • APC 2013/14 to 2015/16 Additional note – third parties The Nuffield Trust will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuffield Trust for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide.	Anticipated dates of study reports are listed. All may also include presentational web material (for example slideshows and blog posts), in addition to presentations given in person at relevant research or policy conferences, etc. 1. Evaluations of the impact of innovations in health and social care on hospital utilisation • - Integrated Care Models (Pioneers).Interim report in 2016, with annual reports from 2016 to 2020 (final report in 2020). - New models of primary care • Interim report, September 2015. Final Nuffield Trust research reports will be available Autumn 2016. Update April 2016: Nuffield Trust publication due in June 2016 and planning a subsequent separate academic research paper - PM’s challenge fund in Barking, Havering and Redbridge • Interim report to local areas, June 2015. Final Nuffield Trust research report, peer review articles - summer 2016. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. - Avoidable harm project • Reports co published with LSHTM. Final report to NIHR available January 2017. Also peer review articles at this time. - Ambulatory emergency care project • Nuffield Trust preliminary report (evaluation of pilot sites) – spring 2015 and if successful we will agree a more comprehensive longer term evaluation proposal. UPDATED OCT 2016: This project is now closed andno longer require processing data for this use (apart from auditing purposes). - Assessment of the burden of alcohol on hospital services • Nuffield Trust report completed by summer 2015, with peer reviewed papers to be submitted also in summer 2015. UPDATED OCT 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. - Descriptions of differential patterns of hospital use by area and provider. • Analysis of length of stay led to workshop in September 2014 (with Monitor), research report planned for winter 2014. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). Child health study, to report 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual outputs will be covered within separate applications 5. Linkage of HES data to HES ids provided by HSCIC regarding a specific cohort Individual outputs will be covered within separate applications 6. Informing the public debate about hospital use The outputs of this work will be in the form of blogs, briefings, and/or presentation slideshows, posted on the Nuffield Trust website and made freely available to all. Due the responsive nature of the work, The Nuffield Trust are not able to provide prospective dates for these outputs 7. Evaluation of Virtual Wards in Devon Nuffield trust report to be published by end of 2015. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 8. Evaluating the implementation of Quality Standards in the London region • Nuffield Trust report to be published by mid-2016 The report will require quantitative analysis, the methods for which will be piloted in a Masters dissertation to be completed by the end of 2015. This work, which is overseen by a senior analyst, will lead to a peer reviewed publication to be submitted also in early 2016. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Outputs in late 2017 will include a final report, an executive summary, and summary results for a lay audience. All will be made publically available on the Nuffield Trust website. Other planned mechanisms for dissemination include: the packaging and provision of on-going feedback to participating hospitals; workshops with user groups; face to face engagement with policy makers at national level; explicit knowledge transfer and exchange initiatives, such as working with networks such as the NHS Confederation. All data published/disseminated will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines. At the same time abstracts will be submitted to key conferences, such as Future Hospital Programme of the RCPL, Quality and Safety in Health Care Forum, the NHS Confederation Conference, as well as NIHR events. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Summary report published on Nuffield Trust website in winter 2016 and with an aim to publish in a reputable, peer reviewed journal by early 2017. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. All data published will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines.	Over the past five years The Nuffield Trust’s research studies, using NHS data, have been widely used to inform decision making and debate in health care. The Nuffield Trust publishes their reports on the Nuffield website and in peer reviewed journals where appropriate. The Nuffield Trust list below recent reports of studies where they have made use of HES data; There are many examples of The Nuffield Trust’s work being cited in parliamentary debates and select committees as well as used by national bodies including the Department of Health and NHS England, CQC and Monitor. Many of the projects have been funded by the Department of Health and NHS, and The Nuffield Trust work in partnership with NHS and other care organisations and with universities. The Nuffield Trust has also provided examples of their studies for NHS Digital to use as evidence to the health select committee. The benefits of The Nuffield Trust’s work are seen in terms of decisions made by healthcare commissioners and providers, when thinking about the types of services needed to deliver benefits to patients, as well as by policy makers. The following provides benefits for each of the projects; 1. Evaluations of the impact of innovations in health and social care on hospital utilisation. The study of Integrated Care Models will inform health care providers, commissioners and policymakers of the impacts of new forms of integrated care emerging under the banner of national ‘Pioneer projects’. This is a piece of applied research funded by DH and with a very wide audience. Throughout the study, the Nuffield Trust will be engaging with the Pioneer sites themselves and with others interested in developing new models of care. This aligns with national policy goals to provide a better care experiences through integration of services. Over the past few years a range of new forms of GP organisations have emerged. These are seen as one solution to systemic problems facing primary care; however there is little empirical analysis of these new organisational forms. The Nuffield Trust’s study of new models of primary care will be looking at the impact of networks of practices in terms of the way they have changed patient care and service delivery. Such analysis will be critical to future NHS planning on how to organise primary care services. The results will be of key interest all those involved in general practice, primary care and the commissioning of out of hospital services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented preliminary results to members of the New Models of Primary Care Network at quarterly meetings. Improving access to primary has been a matter of concern at the highest levels of government. In 2013 the PM created a Challenge Fund to look at new models of care. As evaluation partners of Barking, Havering and Redbridge’s successful bid to adapt services in the local area, The Nuffield Trust’s work will give provide a judgement about how successful these schemes might be. This will directly impact on local commissioners, and have implications across the country. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In all such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The Nuffield Trust’s work on serious avoidable harms has the possibility of contributing to safer care and better methods for future monitoring systems. Ambulatory emergency care (AEC) provides a model of care for patients who have urgent care needs, but do not necessarily warrant an acute hospital admission. Though a number of local evaluative studies have been undertaken there are no systematic analyses across a range of organisations providing AEC. Through analysing the impacts of these schemes we can identify which types are most successful in delivering better care for patients. These can serve as models of success for areas wishing to develop their own services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented findings at British Association of Ambulatory Emergency Care Annual National Conference 2015. This project is now closed andno longer requires processing data for this use (apart from auditing purposes). The Nuffield Trust’s study of alcohol aims to understand the burden on hospital services and to identify areas where preventative action is working well. This work will consider effectiveness of different models of service provision which will meet a specific recommendation for future work made by Public Health England following their survey of hospital alcohol teams. The findings will be of interest to Health and Wellbeing Boards and to local commissioners of alcohol services and aims to share learning of how the burden of alcohol to hospitals can be reduced. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has – (1) Produced a paper "Alcohol-specific activity in hospitals in England" published on 22nd Dec 2015, along with blog "The sobering burden of alcohol on the NHS". (2) Submitted an academic paper to the BMC Public Health "The impact of alcohol care teams on emergency secondary care use following a diagnosis of alcoholic liver disease - a national difference-in-difference study." on 12/1/2016 . (3) Public Health England Conference abstract poster presentation "Hospital use before and after first recorded diagnosis of alcohol related liver disease in England: Opportunities for early intervention to reduce harm". This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. These pieces of work aim to identify good practice in the efficient use of hospital resources. The Nuffield Trust’s future modelling will be used (as their past modelling currently is) to inform public debate about health service provision and central planning assumptions about future needs. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has, through the QualityWatch (http://www.qualitywatch.org.uk/) programme, produced a series of reports, carried out several activities and had the following impact. This includes – 1. Hospital admissions from care homes (29th January 2015). Subsequent to publication this was referenced in the evidence for NICE guidance: Transition between inpatient hospital settings and community or care home settings for adults with social care needs (November 2015) 2. Mental ill health and hospital use (14th October 2015). Nuffield Trust has presented the findings at the Public Health England Conference (September 2015) and at the International Conference of Integrated care (May 2016). Nuffield Trust has also had several contacts with local healthcare providers for guidance on how to apply the methods to carry out the same analysis locally. Whilst it is hard to draw causality, Nuffield have also been made aware of NHS England undertaking work to drive improvements in the quality of physical health care provided by mental health providers to service users with severe and enduring mental ill health and will be developing a national clinical audit to underpin this under the National Mental Health CQUIN scheme for 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual benefits will be covered within separate applications 5. Linkage of HES data to HES IDs provided by HSCIC regarding a specific cohort Individual benefits will be covered within separate applications 6. Informing the public debate about hospital use The Nuffield Trust’s responsive analyses will improve the quality of public debate on hospital use by broadening the available evidence base, focusing the debate on evidence rather than assertion and potentially preventing poor policy decisions. This work is widely reported in the media and Nuffield regularly meet with senior policy-makers and leaders in the NHS to discuss thiswork. 7. Evaluation of Virtual Wards in Devon The evaluation of Virtual Wards fits within a suite of work the Nuffield are undertaking on community based alternatives to hospital care. An earlier study of ours (funded by NIHR) included a very early assessment of Devon’s virtual wards [1], but the number of people recruited was too small to make any robust assessment of whether the scheme had reduced future unplanned admissions. In the following years 6,000 primarily older people have been provided with care in a home based virtual ward. This cohort size should give us the power to detect significant differences, even where these are relatively small. The overall aim is to provide evidence to the health service on methods of care which help people to stay independent of hospital for longer. [1] Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes) 8. Evaluating the implementation of Quality Standards in the London region This work aims to assess the extent of the patient benefits delivered by the introduction of new quality standards and to evaluate a new set of outcome indicators that can be used to more accurately measure their benefits. Applying these standards has a potential direct impact on the quality of over a million care episodes a year – over 500,000 patients. Moreover the London standards will be incorporated into the national Keogh standards. As a consequence this work on standards and outcomes will be of relevance to every hospital in the NHS with prospects of being incorporated into national audit tools. Nuffield will be presenting the outcomes in peer-reviewed reports and using Nuffield's extensive network of contacts to target these towards key decision makers. This will be facilitated by the Trust’s communications team who are very experienced with this type of activity. There is also direct interest from NHS London who have expressed an interest in funding further pieces of work in this area. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Three separate panels of experts, the Independent Commission for the Royal College of General Practitioners and the Health Foundation, The Royal College of Physicians of London’s (RCPL) Future Hospital Commission (FHC) and the General Medical Council’s Shape of Training review, have all recommended a revival in general medicine to better provide high-quality, cost-effective care. However, general medicine cannot be rapidly reintroduced, nor hospitals, let alone smaller ones, reconfigure services unless there is a clear understanding of patient need and how different models of current medical care meet these. With NHS England’s Viable Smaller Hospitals workstream of the New Models of Care programme already underway, there is an urgent need for clear and comprehensive evidence to guide future policy and service reconfiguration. This study has the potential to have a major impact at the national and international levels, as the debate so far around medical generalism has operated largely at an abstract level. This research will provide much needed evidence to ground the debate in the empirical and experiential realities of underlying patient need. It will provide a theoretically informed evidence base from which to take the debate forward. This will not only have relevance for unscheduled adult medical care, but could also be applied to other clinical areas, such as surgery and paediatrics. It also has relevance to larger hospitals, as well as informing much broader, international debates around matching medical workforce to growing patient need. This research should influence decision making around: • Ways of working in hospitals • The education of doctors at undergraduate and graduate level • The number and types of doctors required in the UK • Continuing professional development for physicians • Legislative and contractual arrangements for doctors • The future of smaller hospitals and their role in the wider healthcare system 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Despite the prominence of readmission rates in discourse around health service organisation, there are few empirical studies that compare the rates of readmission across different health systems. This analysis will provide a thorough comparison between readmission rates in England and the Netherlands, and also allow a deeper understanding of which readmissions are truly preventable and those that occur for other reasons. Readmissions due to complications are a burden for patients and account for high healthcare costs: this research will provide insight into the types of readmissions that should really be included in hospital-level indicators to get a valid representation of quality improvement potential. By way of further evidence as to the use made of HES, a non-exhaustive list of recent publication using HES data is provided below. All such articles are in the public domain, and many relate directly to current health practice or topics of interest, and have been commissioned by the NHS or the Department of Health. Nuffield Trust Research Group - Recent publications that have used HES (October 2014) • Holly Dorning and Martin Bardsley. Focus On Allied Health Professionals. Nuffield Trust September 2014. • Theo Georghiou and Martin Bardsley. Exploring the costs of end of life care. Nuffield Trust September 2014. • Ian Blunt, Martin Bardsley & Giovanni FM Strippoli. Predialysis hospital use and late referrals in incident dialysis patients in England: a retrospective cohort study. Nephrology Dialysis Transplantation. Nephrol. Dial. Transplant. (2014) doi: 10.1093/ndt/gfu284 • Steventon A, Bardsley M, Doll H, Tuckey E, Newman S. Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. Submitted for publication. BMC Health Services Research. June 2014 • Steventon A, Bardsley M and Mays N. Effect of a telephonic alert system (Healthy Outlook) for patients with chronic obstructive pulmonary disease: cohort study with matched controls. Journal of Public Health Advance Access published July 10, 2014. pp. 1–9 doi:10.1093/pubmed/fdu042 • Blunt I, Bardsley M, Grove A, et al. Classifying emergency 30-day readmissions in England using routine hospital data 2004–2010: what is the scope for reduction? Emerg Med J Published Online First 26/3/14 doi:10.1136/emermed-2013-202531 • Lewis, G. H., Vaithianathan, R., Wright, L., Brice, M. R., Lovell, P., Rankin, S., & Bardsley, M. (2013, November 4). Integrating care for high-risk patients in England using the virtual ward model: lessons in the process of care integration from three case sites. International Journal of Integrated Care. Retrieved from http://www.ijic.org/index.php/ijic/article/view/URN%3ANBN%3ANL%3AUI%3A10-1-114754/2197 • Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. • Steventon A, Tunkel S, Blunt I and Bardsley M. Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls. BMJ. 2013 Aug 6;347:f4585. doi: 10.1136/bmj.f4585. • Bardsley M, Doll H and Steventon A. Impact of telehealth on general practice contacts: findings from the whole systems demonstrator cluster randomised trial BMC Health Services Research 2013, 13:395 doi:10.1186/1472-6963-13-395 • Roberts A, Blunt I, Bardsley M. Focus On: Distance from home for emergency care. QualityWatch Report May 2014. Nuffield Trust/Health Foundation • Georghiou T, Cooke M, & Bardsley M. How Representative Are Patients Who Access the Marie Curie Nursing Service of the Population of People Who Die Each Year in England? BMJ Supportive & Palliative Care, 3(1), 134–134. doi:10.1136/bmjspcare-2013-000453b.27 • Natasha Curry, Matthew Harris, Laura Gunn, Yannis Pappas, Ian Blunt, Michael Soljak, Nikolaos Mastellos, Holly Holder, Judith Smith, Azeem Majeed, Agnieszka Ignatowicz, Felix Greaves, Athina Belsi, Nicola Costin-Davis, Jessica D Jones Nielsen, Geva Greenfield, Elizabeth Cecil, Susan Patterson, Josip Car, Martin Bardsley Integrated care pilot in north west London: a mixed methods evaluation. Int J Integr Care 2013; Jul–Sep, URN:NBN:NL:UI:10-1-114735 • Chitnis, X. , Georghiou, T., Steventon, A., & Bardsley, M. J. (2013). Effect of a home-based end-of-life nursing service on hospital use at the end of life and place of death: a study using administrative data and matched controls. BMJ Supportive & Palliative Care, 1–9. doi:10.1136/bmjspcare-2012-000424 • Bardsley M, Blunt I, Davies S, Dixon J. Is secondary preventive care improving? Observational study of 10 year trends in emergency admissions for conditions amenable to ambulatory care. BMJ Open 2013; :e002007. doi:10.1136/bmjopen-2012-002007 • Davies A, Chitnis X, Bardsley M. Hospital activity and cost incurred due to unregistered patients in England: considerations for current and new commissioners. J Public Health first published online December 19, 2012 doi:10.1093/pubmed/fds098 • Clarke A, Blunt I, Bardsley M. Analysis of emergency 30 day readmissions in England using routine hospital data 2004-20010.Is there scope for reduction. Presented the Society for Social Medicine Annual Scientific Meeting. Journal of Epidemiology and Community Health. September 2012 Supplement. Doi:10.1136/jech-2012-201753.117 • Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of readmission within 30 days of discharge (PARR-30). BMJ Open. 2012;00:e001667. doi:10.1136/bmjopen-2012-001667. • Roland M, Lewis R, Steventon A, Adams J, Bardsley M, Brereton L, Chitnis X, Staetsky L, Tunkel S, Ling T. Case management for at-risk elderly patients in the English Integrated Care Pilots: observational study of staff and patient experience and secondary care utilisation. International Journal of Integrated Care – Volume 12, 24 July – URN:NBN:NL:UI:10-1-113731 / ijic2012-130 – http://www.ijic.org/ • Steventon A, Bardsley M, Billings J, Dixon J, Doll H, Hirani S, Cartwright M, Rixon L, Knapp M, Henderson C, Rogers A, Fitzpatrick R, Hendy J, Newman S. Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 2012;344:e3874.
THE NUFFIELD TRUST	THE NUFFIELD TRUST	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	The Nuffield Trust is an independent research group overseen by a board of Trustees including a number of senior NHS clinicians, managers and academics. The Nuffield Trust undertakes work for the public good and within a research governance framework. The purposes for receiving HES data falls into the following categories :- 1. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The projects are: • Evaluation of the Integrated Care ‘Pioneers’. These are models of care aimed at reducing the impact of boundaries between care providers. This work is in partnership with the DH Policy Innovation Research Unit based at the London School of Hygiene and Tropical Medicine. • Evaluation of new models of primary care. This is an evaluation of selected ‘scaled up’ GP practices, to explore development of new general practice organisations. • Evaluation of a local scheme to deliver improved care for complex cases – as part of the PM’s Challenge Fund in Barking, Havering and Redbridge (funded by PM’s Challenge Fund). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In these studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The projects are: • A DH funded project to look at ways to identify people who suffered avoidable serious harm. This will test whether HES data can be used as a screening tool to identify cases for specific audit. This work will be undertaken in conjunction with the London School of Hygiene and Tropical Medicine and Imperial College London. • Patterns of urgent care use related to the development of ambulatory emergency medicine, a new approach being adopted in hospitals around the country. The Nuffield Trust will look at the impact on patterns of acute hospital use and long terms outcomes for patients. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). • An assessment of the impact of alcohol on hospital services. This study aims to evaluate whether alcohol is an increasing burden on acute hospital services, will attempt to identify geographical areas where hospital alcohol teams are working well and aims to share opportunities identified for improvement. UPDATED INFORMATION OCT 2016 : This project is now closed and no longer requires the processing data for this use (apart from auditing purposes). Additional projects may be added to this list but will be subject to an amendment to this agreement being approved by NHS Digital. 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. The projects: • Descriptions of differential patterns of hospital use (admissions, lengths of stay, child health indicators) by area and provider. This work aims to identify good practice in the efficient use of hospital resources. 4. Linkage of HES data to linked datasets provided by NHS Digital A number of other projects require access to HES data, but also make use of a linked dataset such as HES-ONS. These are subject to separate applications to NHS Digital, but would not require a separate data release. Instead the applicant would make use of the data provided under this agreement. Such projects typically cover 10 years of HES data. 5. Linkage of HES data to HES ids provided by NHS Digital regarding a specific cohort. Such uses would again be subject to separate data agreements, since they would have a different legal basis and potentially involve patient consent. They would be considered separately under different applications but would not require a separate data release. 6. Informing the public debate about hospital use The Nuffield Trust regularly acts to improve the quality of public debate on use of hospital services by publishing responsive research, which helps focus the debate on evidence. Trigger for this work include a specific issue suddenly coming to national prominence, or an individual or organisation making an assertion which is easily tested using data already available. As an independent research organisation and registered charity, with independence from party politics overseen by the board of trustees, such interventions are carefully considered to ensure that an evidence-based statement may add value to the overall debate. They are not provided at the request of any individual organisation. 7. Evaluations of the impact of innovations in health and social care on hospital utilisation In an effort to improve the quality of health care and reduce the financial pressure on the NHS, efforts are being made to deliver more care in community settings, with the aim of preventing unnecessary and expensive admissions to hospital. The Nuffield Trust is developing methods to evaluate how well these interventions perform. The project involves an evaluation of Virtual Wards in Devon. This study aims to evaluate a multidisciplinary care management scheme which was delivered to individuals in their homes. This work is intended to follow up an earlier study funded by NIHR which was only able to capture the first hundred patients admitted to the Virtual Ward. Over subsequent years, six thousand individuals have been admitted to the scheme. These were chosen as they were judged to have a high risk of hospitalisation. Nuffield will test whether the post-Virtual Ward hospital admissions were low within this group, compared to a similar cohort of people from other parts of the country selected from HES. UPDATED INFORMATION OCT 2016: This project is now closed and no longer requires processing of the data for this use (apart from auditing purposes). 8. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The project involves evaluating the implementation of Quality Standards in the London region, which were developed and introduced to reduce variability in and improve patient care. The study aims to: - investigate variation in outcomes and adherence to standards across London - determine if there is an association between degrees of standard implementation and outcomes - evaluate innovative outcome measures to investigate the impact of standards on patient care and for monitoring standard adherence UPDATED ADDITIONAL PROJECT information OCT 16 The following describes new projects the Nuffield Trust are requesting access to HES data for; 9. Research studies identifying models of medical generalism used in smaller hospitals and exploring their strengths and weaknesses in treating older and more complex patients from patient, professional and service perspectives. The rising numbers of older and more complex patients is considered to be one of the most pressing problems facing the NHS. Although they receive the most resource-intensive care, their problems are less likely to be accurately diagnosed and have more adverse outcomes than other age groups. The emerging consensus is that current models of hospital care, which are heavily based around specialists delivering disease-specific care, serve these patients poorly, as it is often fragmented and poorly co-ordinated. A revival of medical generalism has been suggested to provide better and more cost-effective care. The reality, however, is that there is a paucity of evidence on which to base new models of medical generalism. Smaller hospitals provide an ideal environment in which to investigate models of medical generalist care, as their patient population is older and more vulnerable, while their size creates constraints on their income, capacity and staffing. The overarching aim of this research, therefore, is to identify the models of medical generalism used in smaller hospitals and explore their strengths and weaknesses from patient, professional and service perspectives. More specifically, Nuffield Trust will be using HES data to create a classification of patients that might benefit from general medical care and, based on this classification, provide a descriptive analysis of the workloads of smaller hospitals. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data The number of unplanned emergency readmissions to hospital have often been cited as a marker of quality of hospital care. Indeed, in England, readmissions have been used to influence hospital reimbursements. A number of studies, however, have shown that readmissions are complex and can be linked with a range of factors other than preventable or avoidable harms. If the quality of care at hospital level and individual patient characteristics are not the sole drivers of readmission then the additional factors must lie in the way different health systems manage patients. One way to consider the impact of systemic differences in health systems is to use international comparisons as a form of a natural experiment to see if patterns of readmission are similar or different. Though such comparisons cannot definitely identify the reasons behind differences, they can prompt useful questions on the effects of different health systems. In this analysis Nuffield Trust want to test whether two different health systems demonstrate a fundamentally different pattern of hospital readmissions. Within any health system there are variations in readmissions rates between areas - the product of a host of patient and health system level factors influencing decisions and resource use. In order to understand the impacts of the broader health systems then we need to consider the overall distributions of readmissions and standardise – as far as possible – for differences at the patient level attributable to the underlying health problems. The aim will be to analyse HES data covering admissions to NHS hospitals for selected years and calculate overall readmission rates. In parallel we will calculate the equivalent readmission metrics using the Dutch national data for the same time period. Nuffield Trust will then test for statistically significant differences in readmission rates between the two countries, and quantify to what extent any differences can be explained by patient-level factors (e.g. age, deprivation, severity) and the extent to which any variation could be explained by differences in the two health systems. Nuffield Trust will also attempt to distinguish between potentially preventable readmissions and other reasons for readmissions, using the administrative data, and produce a comparison between the two systems.	The data requested and already disseminated will be accessed and processed by substantive employees of The Nuffield Trust and only for the purposes described in the application. Whilst the nature of detailed analysis in relation to each project varies, the broad context of processing is consistent. In summary :- - The data is downloaded from NHS Digital and imported into SAS. The server is held on-site, and access is restricted to named individuals according to The Nuffield Trust’s security policy. - The data is held within separate folders within the server. - Remote access to the database is permitted, but only through Citrix via secure token (so processing is still carried out on site), and with local printing and downloading disabled. - Only staff who have signed a confidentiality agreement and have received IG training are permitted access. - All access to individual files is recorded, and a sample audited to investigate the existence of any adverse incidents, and ensure that appropriate access has been maintained. - Once held in SAS, the researcher will view the data and select a specific cohort for each individual study. Commonly a process will initially take place to define the particular cohort of interest in terms of e.g. individual diagnostic codes or procedure codes. The researchers will use routinely available filter definitions where possible, but may amend these based on the nature of each study’s group of interest. Depending on the research a similar control group may be established. - The individual researcher then analyses the data, before applying the relevant disclosure controls to any output. Software used will be SAS, R and stata; typically this will involve analysis on several outcome measures, risk adjustment and the construction of control groups. - No record level data would be linked to this dataset, but it may be combined with publically available demographic or geographic data, for example in relation to local Trust performance - Outputs are thus produced which consist of aggregate data (or indicator/statistical data) only. As an example, for the assessment of the burden of alcohol on hospital services, The Nuffield Trust will look at national trends of alcohol related A&E attendances and inpatient admissions for alcohol related liver disease (ARLD) over the most recent decade. The Nuffield Trust will also identify a cohort of patients diagnosed with ARLD for the first time, and examine their prior and post diagnosis patterns of hospital use, in the context of a comparator group. Analyses will be undertaken at local authority level and will take into account provision of acute trust alcohol services. In all such work, The Nuffield Trust analyse patterns of hospital activity by area, by year, by condition or by provider, developing comparative analyses and standardising for a range of episode level, or patient level variables – such as age, the presence of a long terms condition, prior patterns of use. The analyses commonly follow the health and care of a well-defined cohort of individuals over a lengthy period of time (for example the alcohol study will follow for ten years, the child health study – part of the differential patterns of health – will evaluate a cohort throughout childhood). Such analyses require complex processing for fair comparisons and to capture activity for whole populations – something that only nationally collated data can provide. The datasets necessary for each of the studies are listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. *Study will require further years of data beyond those being requested in this application. : - Integrated Care Models (Pioneers). • APC, OP, AE 2004/05 to 2019/20* - New models of primary care • APC, OP, AE 2003/04 to 2015/16 - PM’s challenge fund in Barking, Havering and Redbridge • APC, OP, AE 2004/05 to 2015/16 - Avoidable harms project • APC, OP, AE 2003/04 to 2016/17* - Ambulatory emergency care project. (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2015/16 - Assessment of the burden of alcohol on hospital services (Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes)). • APC, OP, AE 2001/02 to 2013/14 - Descriptions of differential patterns of hospital use by area and provider. • APC, OP, AE 1997/98 to 2015/16 For the study of Virtual Wards in Devon, the Nuffield Trust will use HES data from a small set of areas chosen as being similar to Devon (these areas are Somerset, Cornwall, Shropshire and Herefordshire). From these areas, the researchers will select a pseudo control group of individuals who shared characteristics with individuals who were admitted to the Virtual Ward scheme in Devon. These characteristics include age, sex, prior use of hospital services, and diagnostic history. The characteristics and hospital utilisation of the individuals admitted to Virtual Wards will be derived using a locally sourced pseudonymised data set (SUS). We will then compare future unplanned and other admissions to test for differences between the Devon Virtual Wards cohort and the pseudo control group. For the evaluation of the implementation of Quality Standards in the London region The Nuffield Trust will look at variation in outcomes by London hospital/trust by carrying out cross section analysis at different time points, analysing the changes in extent of variation over time, investigating variations in trends and looking at these in different patient groups. The datasets necessary for the new study is listed below. Where the start date is given as being earlier, OP data will be used from 2003/04 and AE data from 2007/08. - Evaluation of Virtual Wards in Devon. (UPDATED OCT 2016: This project is now closed and no longer require processing data for this use (apart from auditing purposes). • APC, OP, AE 2006/07 to 2014/15 - Evaluating the implementation of Quality Standards in the London region • APC, OP, AE 2005/06 to 2015/16 UPDATED ADDITIONAL PROJECT information OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. - Research studies identifying and evaluating models of medical generalism • APC, OP, AE 2010/11 to 2015/16 - Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data • APC 2013/14 to 2015/16 Additional note – third parties The Nuffield Trust will not provide access to for any third parties to access record level data, even where these third parties are study partners. The use of this data will be limited to Nuffield Trust for the purpose outlined above only. Data published or provided to third parties will be limited to aggregated data, at area, organisational or cohort-level all subject to small number suppression in line with the HES Analysis Guide.	Anticipated dates of study reports are listed. All may also include presentational web material (for example slideshows and blog posts), in addition to presentations given in person at relevant research or policy conferences, etc. 1. Evaluations of the impact of innovations in health and social care on hospital utilisation • - Integrated Care Models (Pioneers).Interim report in 2016, with annual reports from 2016 to 2020 (final report in 2020). - New models of primary care • Interim report, September 2015. Final Nuffield Trust research reports will be available Autumn 2016. Update April 2016: Nuffield Trust publication due in June 2016 and planning a subsequent separate academic research paper - PM’s challenge fund in Barking, Havering and Redbridge • Interim report to local areas, June 2015. Final Nuffield Trust research report, peer review articles - summer 2016. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. - Avoidable harm project • Reports co published with LSHTM. Final report to NIHR available January 2017. Also peer review articles at this time. - Ambulatory emergency care project • Nuffield Trust preliminary report (evaluation of pilot sites) – spring 2015 and if successful we will agree a more comprehensive longer term evaluation proposal. UPDATED OCT 2016: This project is now closed andno longer require processing data for this use (apart from auditing purposes). - Assessment of the burden of alcohol on hospital services • Nuffield Trust report completed by summer 2015, with peer reviewed papers to be submitted also in summer 2015. UPDATED OCT 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. - Descriptions of differential patterns of hospital use by area and provider. • Analysis of length of stay led to workshop in September 2014 (with Monitor), research report planned for winter 2014. Update May 2016: This project is now closed and we no longer require processing data for this use (apart from auditing purposes). Child health study, to report 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual outputs will be covered within separate applications 5. Linkage of HES data to HES ids provided by HSCIC regarding a specific cohort Individual outputs will be covered within separate applications 6. Informing the public debate about hospital use The outputs of this work will be in the form of blogs, briefings, and/or presentation slideshows, posted on the Nuffield Trust website and made freely available to all. Due the responsive nature of the work, The Nuffield Trust are not able to provide prospective dates for these outputs 7. Evaluation of Virtual Wards in Devon Nuffield trust report to be published by end of 2015. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 8. Evaluating the implementation of Quality Standards in the London region • Nuffield Trust report to be published by mid-2016 The report will require quantitative analysis, the methods for which will be piloted in a Masters dissertation to be completed by the end of 2015. This work, which is overseen by a senior analyst, will lead to a peer reviewed publication to be submitted also in early 2016. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Outputs in late 2017 will include a final report, an executive summary, and summary results for a lay audience. All will be made publically available on the Nuffield Trust website. Other planned mechanisms for dissemination include: the packaging and provision of on-going feedback to participating hospitals; workshops with user groups; face to face engagement with policy makers at national level; explicit knowledge transfer and exchange initiatives, such as working with networks such as the NHS Confederation. All data published/disseminated will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines. At the same time abstracts will be submitted to key conferences, such as Future Hospital Programme of the RCPL, Quality and Safety in Health Care Forum, the NHS Confederation Conference, as well as NIHR events. 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Summary report published on Nuffield Trust website in winter 2016 and with an aim to publish in a reputable, peer reviewed journal by early 2017. The Nuffield Trust network will also be used to promote the findings amongst senior decision makers in the NHS and the Department of Health. All data published will be aggregated and no small numbers are anticipated but if they arise then they will be suppressed in line with HES analysis guidelines.	Over the past five years The Nuffield Trust’s research studies, using NHS data, have been widely used to inform decision making and debate in health care. The Nuffield Trust publishes their reports on the Nuffield website and in peer reviewed journals where appropriate. The Nuffield Trust list below recent reports of studies where they have made use of HES data; There are many examples of The Nuffield Trust’s work being cited in parliamentary debates and select committees as well as used by national bodies including the Department of Health and NHS England, CQC and Monitor. Many of the projects have been funded by the Department of Health and NHS, and The Nuffield Trust work in partnership with NHS and other care organisations and with universities. The Nuffield Trust has also provided examples of their studies for NHS Digital to use as evidence to the health select committee. The benefits of The Nuffield Trust’s work are seen in terms of decisions made by healthcare commissioners and providers, when thinking about the types of services needed to deliver benefits to patients, as well as by policy makers. The following provides benefits for each of the projects; 1. Evaluations of the impact of innovations in health and social care on hospital utilisation. The study of Integrated Care Models will inform health care providers, commissioners and policymakers of the impacts of new forms of integrated care emerging under the banner of national ‘Pioneer projects’. This is a piece of applied research funded by DH and with a very wide audience. Throughout the study, the Nuffield Trust will be engaging with the Pioneer sites themselves and with others interested in developing new models of care. This aligns with national policy goals to provide a better care experiences through integration of services. Over the past few years a range of new forms of GP organisations have emerged. These are seen as one solution to systemic problems facing primary care; however there is little empirical analysis of these new organisational forms. The Nuffield Trust’s study of new models of primary care will be looking at the impact of networks of practices in terms of the way they have changed patient care and service delivery. Such analysis will be critical to future NHS planning on how to organise primary care services. The results will be of key interest all those involved in general practice, primary care and the commissioning of out of hospital services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented preliminary results to members of the New Models of Primary Care Network at quarterly meetings. Improving access to primary has been a matter of concern at the highest levels of government. In 2013 the PM created a Challenge Fund to look at new models of care. As evaluation partners of Barking, Havering and Redbridge’s successful bid to adapt services in the local area, The Nuffield Trust’s work will give provide a judgement about how successful these schemes might be. This will directly impact on local commissioners, and have implications across the country. 2. Research studies involving the surveillance of patterns in hospital admission and costs at area level in England, aimed at identifying areas where innovation in service delivery is taking place. In all such studies The Nuffield Trust are seeking to improve the NHS’s ability to identify and implement good practice in terms of efficient and effective health care for patients. The Nuffield Trust’s work on serious avoidable harms has the possibility of contributing to safer care and better methods for future monitoring systems. Ambulatory emergency care (AEC) provides a model of care for patients who have urgent care needs, but do not necessarily warrant an acute hospital admission. Though a number of local evaluative studies have been undertaken there are no systematic analyses across a range of organisations providing AEC. Through analysing the impacts of these schemes we can identify which types are most successful in delivering better care for patients. These can serve as models of success for areas wishing to develop their own services. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has presented findings at British Association of Ambulatory Emergency Care Annual National Conference 2015. This project is now closed andno longer requires processing data for this use (apart from auditing purposes). The Nuffield Trust’s study of alcohol aims to understand the burden on hospital services and to identify areas where preventative action is working well. This work will consider effectiveness of different models of service provision which will meet a specific recommendation for future work made by Public Health England following their survey of hospital alcohol teams. The findings will be of interest to Health and Wellbeing Boards and to local commissioners of alcohol services and aims to share learning of how the burden of alcohol to hospitals can be reduced. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has – (1) Produced a paper "Alcohol-specific activity in hospitals in England" published on 22nd Dec 2015, along with blog "The sobering burden of alcohol on the NHS". (2) Submitted an academic paper to the BMC Public Health "The impact of alcohol care teams on emergency secondary care use following a diagnosis of alcoholic liver disease - a national difference-in-difference study." on 12/1/2016 . (3) Public Health England Conference abstract poster presentation "Hospital use before and after first recorded diagnosis of alcohol related liver disease in England: Opportunities for early intervention to reduce harm". This project is now closed and no longer requires processing data for this use (apart from auditing purposes). 3. Research studies relating to the efficiency of health services and level of competition in the English NHS. These pieces of work aim to identify good practice in the efficient use of hospital resources. The Nuffield Trust’s future modelling will be used (as their past modelling currently is) to inform public debate about health service provision and central planning assumptions about future needs. UPDATE OCT 2016: As part of realising the expected benefits the Nuffield Trust has, through the QualityWatch (http://www.qualitywatch.org.uk/) programme, produced a series of reports, carried out several activities and had the following impact. This includes – 1. Hospital admissions from care homes (29th January 2015). Subsequent to publication this was referenced in the evidence for NICE guidance: Transition between inpatient hospital settings and community or care home settings for adults with social care needs (November 2015) 2. Mental ill health and hospital use (14th October 2015). Nuffield Trust has presented the findings at the Public Health England Conference (September 2015) and at the International Conference of Integrated care (May 2016). Nuffield Trust has also had several contacts with local healthcare providers for guidance on how to apply the methods to carry out the same analysis locally. Whilst it is hard to draw causality, Nuffield have also been made aware of NHS England undertaking work to drive improvements in the quality of physical health care provided by mental health providers to service users with severe and enduring mental ill health and will be developing a national clinical audit to underpin this under the National Mental Health CQUIN scheme for 2016/17. 4. Linkage of HES data to linked datasets provided by HSCIC Individual benefits will be covered within separate applications 5. Linkage of HES data to HES IDs provided by HSCIC regarding a specific cohort Individual benefits will be covered within separate applications 6. Informing the public debate about hospital use The Nuffield Trust’s responsive analyses will improve the quality of public debate on hospital use by broadening the available evidence base, focusing the debate on evidence rather than assertion and potentially preventing poor policy decisions. This work is widely reported in the media and Nuffield regularly meet with senior policy-makers and leaders in the NHS to discuss thiswork. 7. Evaluation of Virtual Wards in Devon The evaluation of Virtual Wards fits within a suite of work the Nuffield are undertaking on community based alternatives to hospital care. An earlier study of ours (funded by NIHR) included a very early assessment of Devon’s virtual wards [1], but the number of people recruited was too small to make any robust assessment of whether the scheme had reduced future unplanned admissions. In the following years 6,000 primarily older people have been provided with care in a home based virtual ward. This cohort size should give us the power to detect significant differences, even where these are relatively small. The overall aim is to provide evidence to the health service on methods of care which help people to stay independent of hospital for longer. [1] Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. UPDATE OCT 2016: This project is now closed and no longer requires processing data for this use (apart from auditing purposes) 8. Evaluating the implementation of Quality Standards in the London region This work aims to assess the extent of the patient benefits delivered by the introduction of new quality standards and to evaluate a new set of outcome indicators that can be used to more accurately measure their benefits. Applying these standards has a potential direct impact on the quality of over a million care episodes a year – over 500,000 patients. Moreover the London standards will be incorporated into the national Keogh standards. As a consequence this work on standards and outcomes will be of relevance to every hospital in the NHS with prospects of being incorporated into national audit tools. Nuffield will be presenting the outcomes in peer-reviewed reports and using Nuffield's extensive network of contacts to target these towards key decision makers. This will be facilitated by the Trust’s communications team who are very experienced with this type of activity. There is also direct interest from NHS London who have expressed an interest in funding further pieces of work in this area. UPDATED ADDITIONAL PROJECTS OCT 2016 – new projects the Nuffield Trust are requesting access to HES data for. 9. Research studies identifying and evaluating models of medical generalism Three separate panels of experts, the Independent Commission for the Royal College of General Practitioners and the Health Foundation, The Royal College of Physicians of London’s (RCPL) Future Hospital Commission (FHC) and the General Medical Council’s Shape of Training review, have all recommended a revival in general medicine to better provide high-quality, cost-effective care. However, general medicine cannot be rapidly reintroduced, nor hospitals, let alone smaller ones, reconfigure services unless there is a clear understanding of patient need and how different models of current medical care meet these. With NHS England’s Viable Smaller Hospitals workstream of the New Models of Care programme already underway, there is an urgent need for clear and comprehensive evidence to guide future policy and service reconfiguration. This study has the potential to have a major impact at the national and international levels, as the debate so far around medical generalism has operated largely at an abstract level. This research will provide much needed evidence to ground the debate in the empirical and experiential realities of underlying patient need. It will provide a theoretically informed evidence base from which to take the debate forward. This will not only have relevance for unscheduled adult medical care, but could also be applied to other clinical areas, such as surgery and paediatrics. It also has relevance to larger hospitals, as well as informing much broader, international debates around matching medical workforce to growing patient need. This research should influence decision making around: • Ways of working in hospitals • The education of doctors at undergraduate and graduate level • The number and types of doctors required in the UK • Continuing professional development for physicians • Legislative and contractual arrangements for doctors • The future of smaller hospitals and their role in the wider healthcare system 10. Classifying readmissions and comparing readmission rates between the Netherlands and the UK based on national administrative data Despite the prominence of readmission rates in discourse around health service organisation, there are few empirical studies that compare the rates of readmission across different health systems. This analysis will provide a thorough comparison between readmission rates in England and the Netherlands, and also allow a deeper understanding of which readmissions are truly preventable and those that occur for other reasons. Readmissions due to complications are a burden for patients and account for high healthcare costs: this research will provide insight into the types of readmissions that should really be included in hospital-level indicators to get a valid representation of quality improvement potential. By way of further evidence as to the use made of HES, a non-exhaustive list of recent publication using HES data is provided below. All such articles are in the public domain, and many relate directly to current health practice or topics of interest, and have been commissioned by the NHS or the Department of Health. Nuffield Trust Research Group - Recent publications that have used HES (October 2014) • Holly Dorning and Martin Bardsley. Focus On Allied Health Professionals. Nuffield Trust September 2014. • Theo Georghiou and Martin Bardsley. Exploring the costs of end of life care. Nuffield Trust September 2014. • Ian Blunt, Martin Bardsley & Giovanni FM Strippoli. Predialysis hospital use and late referrals in incident dialysis patients in England: a retrospective cohort study. Nephrology Dialysis Transplantation. Nephrol. Dial. Transplant. (2014) doi: 10.1093/ndt/gfu284 • Steventon A, Bardsley M, Doll H, Tuckey E, Newman S. Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. Submitted for publication. BMC Health Services Research. June 2014 • Steventon A, Bardsley M and Mays N. Effect of a telephonic alert system (Healthy Outlook) for patients with chronic obstructive pulmonary disease: cohort study with matched controls. Journal of Public Health Advance Access published July 10, 2014. pp. 1–9 doi:10.1093/pubmed/fdu042 • Blunt I, Bardsley M, Grove A, et al. Classifying emergency 30-day readmissions in England using routine hospital data 2004–2010: what is the scope for reduction? Emerg Med J Published Online First 26/3/14 doi:10.1136/emermed-2013-202531 • Lewis, G. H., Vaithianathan, R., Wright, L., Brice, M. R., Lovell, P., Rankin, S., & Bardsley, M. (2013, November 4). Integrating care for high-risk patients in England using the virtual ward model: lessons in the process of care integration from three case sites. International Journal of Integrated Care. Retrieved from http://www.ijic.org/index.php/ijic/article/view/URN%3ANBN%3ANL%3AUI%3A10-1-114754/2197 • Lewis GH, Georghiou T, Steventon A, Vaithianathan R, Chitnis X, Billings J, et al. Analysis of virtual wards: a multidisciplinary form of case management that integrates social and health care. Final report. NIHR Service Delivery and Organisation Programme; 2013. • Steventon A, Tunkel S, Blunt I and Bardsley M. Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls. BMJ. 2013 Aug 6;347:f4585. doi: 10.1136/bmj.f4585. • Bardsley M, Doll H and Steventon A. Impact of telehealth on general practice contacts: findings from the whole systems demonstrator cluster randomised trial BMC Health Services Research 2013, 13:395 doi:10.1186/1472-6963-13-395 • Roberts A, Blunt I, Bardsley M. Focus On: Distance from home for emergency care. QualityWatch Report May 2014. Nuffield Trust/Health Foundation • Georghiou T, Cooke M, & Bardsley M. How Representative Are Patients Who Access the Marie Curie Nursing Service of the Population of People Who Die Each Year in England? BMJ Supportive & Palliative Care, 3(1), 134–134. doi:10.1136/bmjspcare-2013-000453b.27 • Natasha Curry, Matthew Harris, Laura Gunn, Yannis Pappas, Ian Blunt, Michael Soljak, Nikolaos Mastellos, Holly Holder, Judith Smith, Azeem Majeed, Agnieszka Ignatowicz, Felix Greaves, Athina Belsi, Nicola Costin-Davis, Jessica D Jones Nielsen, Geva Greenfield, Elizabeth Cecil, Susan Patterson, Josip Car, Martin Bardsley Integrated care pilot in north west London: a mixed methods evaluation. Int J Integr Care 2013; Jul–Sep, URN:NBN:NL:UI:10-1-114735 • Chitnis, X. , Georghiou, T., Steventon, A., & Bardsley, M. J. (2013). Effect of a home-based end-of-life nursing service on hospital use at the end of life and place of death: a study using administrative data and matched controls. BMJ Supportive & Palliative Care, 1–9. doi:10.1136/bmjspcare-2012-000424 • Bardsley M, Blunt I, Davies S, Dixon J. Is secondary preventive care improving? Observational study of 10 year trends in emergency admissions for conditions amenable to ambulatory care. BMJ Open 2013; :e002007. doi:10.1136/bmjopen-2012-002007 • Davies A, Chitnis X, Bardsley M. Hospital activity and cost incurred due to unregistered patients in England: considerations for current and new commissioners. J Public Health first published online December 19, 2012 doi:10.1093/pubmed/fds098 • Clarke A, Blunt I, Bardsley M. Analysis of emergency 30 day readmissions in England using routine hospital data 2004-20010.Is there scope for reduction. Presented the Society for Social Medicine Annual Scientific Meeting. Journal of Epidemiology and Community Health. September 2012 Supplement. Doi:10.1136/jech-2012-201753.117 • Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of readmission within 30 days of discharge (PARR-30). BMJ Open. 2012;00:e001667. doi:10.1136/bmjopen-2012-001667. • Roland M, Lewis R, Steventon A, Adams J, Bardsley M, Brereton L, Chitnis X, Staetsky L, Tunkel S, Ling T. Case management for at-risk elderly patients in the English Integrated Care Pilots: observational study of staff and patient experience and secondary care utilisation. International Journal of Integrated Care – Volume 12, 24 July – URN:NBN:NL:UI:10-1-113731 / ijic2012-130 – http://www.ijic.org/ • Steventon A, Bardsley M, Billings J, Dixon J, Doll H, Hirani S, Cartwright M, Rixon L, Knapp M, Henderson C, Rogers A, Fitzpatrick R, Hendy J, Newman S. Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 2012;344:e3874.
THE NUFFIELD TRUST	THE NUFFIELD TRUST	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	The Nuffield Trust’s overarching purpose is to help provide objective research and analysis that boosts the quality of health policy and practice, and ultimately improves the health and health care of people in the UK. The Nuffield Trust are an independent research group overseen by a board of Trustees including a number of senior NHS clinicians, managers and academics. This application relates to a piece of evaluation work the Nuffield Trust is undertaking funded Royal Voluntary Service (RVS) to evaluate their ‘Home from Hospital’ programme in Leicestershire. In order to do this the Nuffield Trust propose using linked data to compare the outcomes for project beneficiaries versus a matched control group who did not receive the scheme’s services. The primary outcome measures will be hospital activity in the period after referral to the scheme. The Nuffield Trust have a successful track record in such studies – most recently in the evaluation of the Cabinet Office and NHS England funded scheme to test the impact of volunteers on hospital utilisation. This work will add to the growing evidence base on the effectiveness of increasingly common programmes in use in the English NHS. The Hospital from Home scheme commenced in October 2014 and runs into early 2017. The key output of the Nuffield Trusts work will be an independent assessment of the strength of evidence that RVS’s scheme has any favourable impact on older people’s use of hospital services. The Nuffield Trust will not be sharing any data with any third party. All published or otherwise shared information will aggregated data with small numbers suppressed in line with the HES Analysis guidance. The Royal Voluntary Service was founded in 1938 to support local communities. With over 35,000 volunteers, their aim is to help older people stay active, independent and able to continue to contribute to society.	The role of NHS Digital: NHS Digital will receive identifiable person level information from RVS. The person level information received by NHS Digital will cover all people recruited to the programme between October 2014 and early 2017, where those individuals have consented to sharing data with NHS Digital for evaluation purposes (subsequently referred to as service recipients). The transferred information will consist of only: • NHS number (if available) • Name • date of birth • address including postcode • gender in addition to a non identifiable client ID added by the services to denote each unique individual. NHS Digital will receive no other information about any service recipient. This data will be transferred to NHS Digital using the NHS Digital’s own secure transfer facilities, under NHS Digital advice. NHS Digital’s Data Linkage Service will process the person identifiers. For each service recipient, they will find the relevant pseudonymised identifier, the HESID, in the form held by the Nuffield Trust. The Data Linkage Service will produce a file intended for the Nuffield Trust. This file will contain the HESID of each service recipient, alongside other limited pseudonymised information: • LSOA of residence, • age (/year of birth) • gender. It will also include information about the matching technique, and the non identifiable client ID. NHS Digital will finally transfer this file securely to the Nuffield Trust. Role of NHS Digital summary: NHS Digital will receive personal identifiers of people recruited to the RVS programme and will provide the relevant pseudonymised HESIDs of these individuals to the Nuffield Trust. The role of the Nuffield Trust The Nuffield Trust will receive from NHS Digital the list of HESIDs of people recruited to the services between October 2014 and early 2017 (service recipients). The Nuffield Trust will receive at the same time a dataset from RVS. This dataset will contain no identifiable information, and will only contain the non identifiable client ID as a person identifier. Other data contained in these files will include non identifiable details of services received by the people recruited to these services, eg date of referral to the service provider, number of minutes of support received, dates of services provided, etc. As a first processing step the Nuffield Trust will link the HESIDs (from NHS Digital) and the non identifiable service information (from the seven service providers) using the non identifiable client ID. The Nuffield Trust will then link this data to pseudonymised HES data and monthly MMES. For each service recipient the Nuffield Trust will link to up to two years of HES data for the period just prior to the referral to the service provider. The Nuffield Trust will also link to all subsequent HES activity captured using the latest monthly HES datasets. Therefore the Nuffield Trust will need to link to HES/MMES APC, OP and A&E data from 2012/13 through to 2016/17 (M13). Using this data the Nuffield Trust will build person level analysis datasets which will characterise each service recipient in terms of: demographic characteristics, history of hospital use, and morbidity characteristics. The key aim of the Nuffield Trust processing is to find a matched group of people who very closely share these same characteristics, but who did not receive a service from RVS, and to use this group as a pseudo-control group. To do this the Nuffield Trust will build analysis datasets for the wider group of people in the hospital in which RVS’s services were offered but where the individuals did not receive a service, or for very similar hospitals, as defined by predictive risk models. From within this group the Nuffield Trust will use prognostic matching and risk modelling techniques to find a ‘closest match’ control cohort for each of the seven intervention cohorts. Once the Nuffield Trust have identified the matched control groups, the Nuffield Trust will analyse all subsequent hospital activity – following recruitment to the service - comparing the behaviour of the intervention group (the actual service recipients) to the matched control group. The Nuffield Trust will focus especially on differences between the two groups in terms of emergency hospital admissions, readmissions, lengths of stay and A&E attendances. Role of Nuffield Trust summary: The Nuffield trust will identify characteristics of all RVS service recipients using HES data from the period prior to recruitment to the service. The Nuffield Trust will find very closely matched control groups, from the wider population. The Nuffield Trust will finally test for differences in subsequent hospital activity between the service recipients and the matched control group. Only substantive employees of the Nuffield Trust will have access to the data and only for the purposes described in this document. Note about third parties: At no point will person level data be transferred from the Nuffield Trust to any other external organisation. All information published and/or passed to partner organisations will be aggregated with small numbers suppressed as required in HES guidance. The Nuffield Trust shall ensure access to data disseminated by NHS Digital is strictly prohibited and must not be accessed by Wavex Technology.	The prime analytical outcome of the Nuffield Trust data processing will be the identification of a group of people (within the HES data) who had extremely similar characteristics to the five projects’ service recipients, but who didn’t receive a service. For example if 1,500 people by early 2017 received a service from RVS, Nuffield will have identified this group in HES data, in addition to another 1,500 individuals to be used as our evaluation’s matched control group. Differences between these two groups in terms of their subsequent use of hospital services will be analysed to test whether there is any evidence that the services have had any impact on admissions to hospital or lengths of stay, adjusting for remaining differences between the two groups. An evaluation report will be submitted to RVS in the second half of 2017. Shortly afterward, the Nuffield Trust will publish the results in a brief Nuffield Trust branded research report, possibly as part of a summary document reviewing all Nuffield Trust recent analyses of the impact of voluntary sector lead schemes. This will be made freely available on the Nuffield Trust website. The Nuffield Trust may also submit the findings to quality peer reviewed journals.	The Nuffield Trust evaluation will provide an independent assessment of the impact of RVS’s Home from Hospital Scheme. This scheme aims to use volunteers and voluntary sector staff to help support older people’s timely discharge from hospital wards. It is one of a number of similar schemes increasingly being commissioned by local authorities, CCGs and acute trusts in England. The evidence the Nuffield Trust provide will be of benefit to these commissioners, both in England and beyond, as they consider funding these types of services over others on a longer-term basis. It may also provide new information for RVS and other charities about specific aspects of their scheme that appear to provide the greatest benefits. The evaluation itself will measure the impact of the schemes on hospital usage: including emergency admissions, re-admissions, length of stay and A&E attendances over ~9 to 12 months post referral. Evidence of the success or failure of the service will be beneficial to patients in the wider sense that if such schemes are shown to lead to improved care after admission to hospital, similar schemes will be more likely to receive funding from local commissioners elsewhere. The Nuffield Trust analyses may help these schemes identify specific subgroups of individuals who might be best targeted for care. This study will be a useful addition to a growing set of Nuffield Trust evidence on the impact of the voluntary sector on use of the acute sector in England: evaluation of British Red Cross ‘Support at Home’ scheme (2012); evaluation of a Cabinet Office ‘social action’ fund (2016), evaluation of Age UK’s Integrated Care Programme (findings due 2017).
THE ROYAL COLLEGE OF SURGEONS OF ENGLAND	THE ROYAL COLLEGE OF SURGEONS OF ENGLAND	MRIS - List Cleaning Report	Identifiable	Non Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	Quality Health Limited require a MRIS List Cleaning Report to carry out mortality checks and retrieve the current patient address for those men in the National Prostate Cancer Audit survey cohort, who have not raised type 2 objections, in England for the purpose of administering a Patient Reported Outcome Measures (PROM) survey of men diagnosed with prostate cancer. Before a questionnaire is sent out to a patient at their home address deceased checks are required to minimise any distress caused to bereaved families. Due to the time lag between diagnosis and the administration of the questionnaire it is also necessary to ensure the patient address held is current. The National Prostate Cancer Audit (NPCA) was commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical Audit Programme. The Royal College of Surgeons was awarded the contract for the audit, which started on 1st April 2013, and is managed as a partnership between a team of clinical, cancer information and audit experts from the British Association of Urological Surgeons, the British Uro-oncology Group, the National Cancer Registration Service and the Royal College of Surgeons. Other stakeholder groups include British Association of Urological Nurses, Prostate Cancer UK, and Tackle Prostate Cancer. The British Association of Urological Surgeons and the British Uro-oncology Group are not considered to be data controllers for the purpose of this application and along with other stakeholder groups will only have access to aggregated outputs with small numbers suppressed. The overall aim of the NPCA and patient survey is to assess the process of care and its outcomes in men diagnosed with prostate cancer in England and Wales. The principal audit questions examine the following against the available appropriate national clinical standards: • service delivery and organisation of care in England and Wales • characteristics of newly-diagnosed prostate cancer, how the cancer was detected and the referral pathway • diagnostic and staging process and planning of initial treatment • initial treatments received • patient experience and health outcomes 18 months after diagnosis • overall and disease-free survival • feasibility of a Prostate Specific Antigen (PSA) testing audit in primary care The collection of patient-reported outcome measures (PROMs) was included in the NPCA because radical treatment for prostate cancer may adversely affect sexual function, urinary continence, and bowel function. These outcomes are seen as important performance indicators of the management of men with localised treatment as they are available relatively soon in the course of the disease. In contrast, it would take many years before performance indicators based on (the absence of) disease-progression or survival will become meaningful.	Quality Health Limited will submit a data file to NHS Digital containing limited Patient Identifiable data fields (name, surname, date of birth, postcode, gender and NHS number) for patients in the NPCA PROMs cohort on a monthly basis. NHS Digital will perform a list-cleaning service and provide Quality Health Limited with the latest demographic details including fact of death and confirmation of address details for those patients who have not raised a type 2 objection. Access to the data is limited to Quality Health Limited and will only be used for the purpose of this Agreement. The administration of the questionnaire will be carried out by Quality Health Limited who will act as a central data collection centre. For the cohort of men identified from the NPCA prospective audit, Public Health England will transfer minimal patient identifiable information including names and addresses, post code, date of birth, NHS number and a unique NPCA identifier to Quality Health Limited. Quality Health Limited will pass these details on to NHS Digital for list cleaning as described above, which will be performed and data returned to Quality Health Limited on a monthly basis. Following receipt of the completed questionnaires, Quality Health Limited will provide collated pseudonymised response data to the NPCA team in the Clinical Effectiveness Unit (CEU) at the Royal College of Surgeons (RCS) for analysis, which will be subsequently linked by the NPCA team to pseudonymised patient-level data from NPCA prospective audit data provided by Public Health England on the basis of unique NPCA identifiers and age at diagnosis (years). Description of overall dataflow for the NPCA patient survey: Patient-level data are submitted by NHS Trusts to the National Cancer Registration and Analysis Service (NCRAS), which is run by Public Health England (the NPCA data collection partner) as part of the NPCA prospective audit on a monthly basis. The NPCA team at the RCS do not receive any confidential patient identifiers in the routine extracts of pseudonymised NPCA patient-level audit data provided by NCRAS, PHE. Confidential patient identifiers (NHS number, patient name, address, postcode and date of birth) for the patients in the NPCA cohort are securely transferred by NCRAS to Quality Health Limited for the purpose of administering the NPCA patient survey. Section 251 approval (15_CAG_0143_NPCa Patient Survey) is in place for the transfer of minimal patient data (collected as part of the NPCA prospective audit) to enable Quality Health Limited to remove any duplicates and to carry out the following: 1. Before a questionnaire is sent out to a patient at their home address Quality Health Limited are required to undertake deceased checks to minimise any distress caused to bereaved families. 2. Due to the time lag between diagnosis and administration of the questionnaire it is also necessary to ensure the patient address held is current. Data collection in England started in October 2015. A response rate of 75% was achieved in the first six months of the NPCA patient survey demonstrating the successful engagement of patients in the collection of NPCA PROMs/PREMs (Patient Reported Experience Measures) data. Further to the instruction of the Secretary of State to NHS Digital in April 2016 to remove the records of patients who have requested a type 2 opt out, the dataflow from NCRAS to Quality Health Limited ceased as NCRAS are unable to identify patients who have raised a type 2 objection and in order to adhere to upholding type 2 objections it is necessary for the RCS to request an MRIS List Clean via NHS Digital. As agreed with NHS Digital, this DARs application is to substitute Quality Health Limited's access to NHS Digital's PDS/DBS checking service with NHS Digital's listing cleaning service. Quality Health Limited would submit a data file to NHS Digital containing limited PI data fields (name, surname, date of birth, postcode and NHS number) for patients in the NPCA PROMs cohort on a monthly basis. The objective of the processing is to provide the latest demographic details including fact of death and confirmation of address details only for those men in the NPCA survey cohort who have NOT raised type 2 objections in England. Only substantive employees of Quality Health Limited would have access to the List Cleaning data returned form NHS Digital. The List Cleaning data supplied from NHS Digital will not be accessed, processed or stored overseas. Following receipt of the completed questionnaires, Quality Health Limited provide collated pseudonymised NPCA PROMs/PREMs survey response data to the CEU without confidential patient identifiable information. The data flow to the NPCA is pseudonymised. None of the datasets received by the NPCA at the Clinical Effectiveness Unit (CEU), RCS contain confidential patient identifiers. There is separation of identifiers and analysis datasets in the data flow. This includes extracts of NPCA prospective audit data provided by the NCRAS and NPCA PROMs/PREMs response data provided by Quality Health Limited. NHS number, patient name, addresses, date of birth, hospital patient ID and postcode are not included in any extract of NPCA data received by the CEU for analysis. The NPCA team at the CEU link pseudonymised NPCA PROMs/PREMs survey response data with pseudonymised NPCA prospective audit clinical data on the basis of a unique NPCA identifier and age at diagnosis (years). Patient cohorts: From the NPCA prospective audit patient population, the following cohorts of men in England are identified for the NPCA PROMs/PREMs: Men diagnosed with localised (non-metastatic) prostate cancer (ICD-10 diagnostic code of C61 - malignant neoplasms of the prostate) between: 1) 1 April 2014 and 31 March 2015 who underwent radical treatment (n = 15,000), and 2) 1 April 2015 and 31 March 2016 who are candidates for radical treatment (irrespective of treatment) (n = 25,000) Questionnaires are sent to men 18 months after diagnosis. Prior to the implementation of type II objections by NHS Digital, surveys were previously sent to men diagnosed between 1 April 2014 and 31st September 2014. A response rate of 75% was achieved.	The results of the NPCA are published in annual reports which are available on the audit’s website (www.npca.org.uk) and are sent directly to all extended Trust teams providing prostate cancer care throughout England. The extended Trust teams include members of clinical teams (including Lead Clinicians and MDT coordinators), representatives from clinical audit departments (including clinical audit managers) and cancer services (including cancer managers). The NPCA professional bodies (British Association of Urologists, British Uro-Oncology Group), data collection partner (The National Cancer Registration and Analysis Service) and stakeholder groups (including British Association of Urological Nurses Prostate Cancer UK, Tackle Prostate Cancer, Public Health England, Health Quality Improvement Partnership) have representation on the NPCA’s Clinical Reference Group and contribute to the development of the annual report. All relevant professional bodies and stakeholder groups receive the final results presented in the annual report under embargo prior to publication for dissemination. The target publication date of the next NPCA Annual Report is November 2017. Publication of the annual report is accompanied by press activities including national media, relevant professional bodies and stakeholder organisations in order to maximise accessibility to the widest audience. Summaries of these reports are also prepared for patients and the general public and available on the audit website alongside additional information. NPCA results are also communicated in conference presentations, peer-reviewed publications and MD/PhD theses.	The benefit of the list-cleaning service is that deceased individuals will be removed from the mail-outs, the latest addresses for patients will be used and only patients that have not raised a type II objection will be sent a survey. This minimises the likelihood of any distress caused to bereaved families and ensures that questionnaires are distributed to the correct address. Overall, the project has benefits which are namely: 1. To evaluate the quality of prostate cancer services in England 2. To determine the variance in the process and outcomes of prostate cancer care in England for the purpose of driving quality improvement 3. To publish comparative local outcomes, along with associated commentary, to enable patients to understand the quality of care being offered when making treatment decisions with regard to their prostate cancer care The findings from these analyses will contribute to changes in clinical practice ensuring that patients receive the best care possible and experience an improved quality of life. Patient-reported outcomes and experience measures (PROMs and PREMs) following radical local treatment of prostate cancer are collected using a survey distributed to individual patients in England 18 months after diagnosis and subsequent treatment. They provide valuable information enabling clinicians to assess the overall effects of treatment and health care providers to study and understand variation in practice and outcomes in different geographical areas. The outputs will be reported in the NPCA annual report due for publication in November 2017. The NPCA outcomes are published in annual reports and scientific journals. The intended audience are clinicians, healthcare professionals, Medical Directors, Chief Executives, audit managers, commissioners, NHS England, Welsh Assembly Government, public and patients. Trusts and Health Boards will use the outcomes in the annual reports to assess the care they provide.
UCL INSTITUTE OF EDUCATION (IOE)	UCL INSTITUTE OF EDUCATION (IOE)	MRIS - Cause of Death Report	Identifiable	Sensitive	Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007	Ongoing	Y	British Cohort Study 1970 (BCS70) is a national longitudinal study which provides the medical and social science community with a set of data comprising information about the lives of its cohort members and their parents. This data set can be used to investigate factors which influence physical, psychological, educational and social development and outcomes. Since 1970, there have been three subsequent attempts to gather information from the full cohort. With each successive attempt, the scope of enquiry has broadened from a strictly medical focus at birth, to encompass physical and educational development at the ages of ten and sixteen. Aims and Investigations 1) To monitor child development educational, physical and psychological in the 1970s, in comparison with those made by the two previous surveys during the 1950s and 1960s. There is a desirability of further study of early hospitalisation, e.g. maternal employment, immunisation and vaccination, housing conditions. 2) To analyse via in-depth studies, with comparison of special groups from the 1958 Study, involving 'deprived' children or children in anomalous family situations e.g. children in care, illegitimate, fostered, from one-parent families, or socially disadvantaged. 3) To examine associations between high-risk medical and social factors in the perinatal period and subsequent child development, e.g. smoking in pregnancy, X-rays etc. This type of analysis, as in any longitudinal investigation, involves gathering data on a large range of 'intermediate variables', both social and environmental, which might affect both the 'casual' factor and the outcome. 4) To identify special groups in childhood who fail to use the services provided by DHSS and DES e.g. schooling, health centres, dental care, speech therapy, etc. and the availability of these services to children in different areas, e.g. urban/rural districts. 5) To analyse regional variations in many factors on which data are available. The needs of pre-school children, for example, may vary from one region to the next on account of differences in the degree of urbanisation, level and type of industrialisation, family incomes, educational and housing policies, etc. This type of information would be of great value in the administrative and policy-making areas of local government.
UCL INSTITUTE OF EDUCATION (IOE)	UCL INSTITUTE OF EDUCATION (IOE)	MRIS - Cohort Event Notification Report	Identifiable	Non Sensitive	Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007	Ongoing	Y	British Cohort Study 1970 (BCS70) is a national longitudinal study which provides the medical and social science community with a set of data comprising information about the lives of its cohort members and their parents. This data set can be used to investigate factors which influence physical, psychological, educational and social development and outcomes. Since 1970, there have been three subsequent attempts to gather information from the full cohort. With each successive attempt, the scope of enquiry has broadened from a strictly medical focus at birth, to encompass physical and educational development at the ages of ten and sixteen. Aims and Investigations 1) To monitor child development educational, physical and psychological in the 1970s, in comparison with those made by the two previous surveys during the 1950s and 1960s. There is a desirability of further study of early hospitalisation, e.g. maternal employment, immunisation and vaccination, housing conditions. 2) To analyse via in-depth studies, with comparison of special groups from the 1958 Study, involving 'deprived' children or children in anomalous family situations e.g. children in care, illegitimate, fostered, from one-parent families, or socially disadvantaged. 3) To examine associations between high-risk medical and social factors in the perinatal period and subsequent child development, e.g. smoking in pregnancy, X-rays etc. This type of analysis, as in any longitudinal investigation, involves gathering data on a large range of 'intermediate variables', both social and environmental, which might affect both the 'casual' factor and the outcome. 4) To identify special groups in childhood who fail to use the services provided by DHSS and DES e.g. schooling, health centres, dental care, speech therapy, etc. and the availability of these services to children in different areas, e.g. urban/rural districts. 5) To analyse regional variations in many factors on which data are available. The needs of pre-school children, for example, may vary from one region to the next on account of differences in the degree of urbanisation, level and type of industrialisation, family incomes, educational and housing policies, etc. This type of information would be of great value in the administrative and policy-making areas of local government.
UK BIOBANK	UK BIOBANK	MRIS - List Cleaning Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	MRIS - Cause of Death Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	MRIS - Members and Postings Report	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	Ongoing	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Hospital Episode Statistics Accident and Emergency	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Hospital Episode Statistics Admitted Patient Care	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Diagnostic Imaging Dataset	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Hospital Episode Statistics Outpatients	Identifiable	Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
UK BIOBANK	UK BIOBANK	Hospital Episode Statistics Critical Care	Identifiable	Non Sensitive	Informed Patient consent to permit the receipt, processing and release of data by the HSCIC	One-Off	N	UK Biobank, a registered charity, is one of the world’s largest medical research resources http://www.ukbiobank.ac.uk/. It was established in 2003. It is funded by the Wellcome Trust, the Department of Health and the Medical Research Council. The recruitment of its 500,000 participants, aged between 45 and 69, took place between 2007 and 2010, whose health is now being followed long term. Access to the resource opened in 2012, since which time there have been over 300 approved applications and over 2,700 researchers have registered to use the resource. The overall purpose of the research is to create a prospective epidemiological resource of 500,000 people aged 45 -69 from around the UK. The methods to be used for the identification, invitation and assessment of participants have been developed following extensive consultation with leading research groups in the UK and internationally, and with research participants and representatives of the general population. All participants in the UK Biobank study have given fully informed consent for access to and long-term storage of information from their medical and other health-related records, and use of this and other information for health-related research purposes; long-term storage and use of their blood and urine samples for health-related research purpose; and recontact by UK Biobank to invite them (but without any obligation) to answer further questions or take part in further assessments. A copy of the consent form is available in the DAAG pack. UK Biobank is a unique resource, because of the combination of its very large size and the range and detail of data that it contains on its participants, including extensive phenotype and genotype information on each of them. Further, rather than recruiting participants with specific diseases or risk factors, UK Biobank is a prospective population-based resource established to support research into the causes of a wide range of diseases affecting people in middle and older age. Thus, the ability to link to participant’s health records, so that specific outcomes occurring during long-term follow-up of the participants can be identified and appropriate disease-based research carried out, is critical. UK Biobank’s priority is to combine extensive and precise measurement of exposures, along with the detailed and rigorous follow-up for a wide range of health related outcomes, and to promote innovative science by maximising secure access to deidentified data. Participants are now being followed through fully approved linkages to death and cancer registries. Flagging of the cohort with these data sources in England Wales and Scotland has been performed with the data provision ongoing. UK Biobank obtained at baseline and holds identifiable information on its participants (with an appropriately high level of security and monitored restricted access to a few, named and approved individuals). It is very important for UK Biobank to be able to maintain as accurate information as possible about participants contact details (including postal address) and registered GP practice (which were up to date for the whole cohort at the time of recruitment but there will be undoubtedly have been changes since then. There are several reasons for this requirement: - Ongoing approved linkages to primary care data require accurate information about registered GP practice (as practice as well as PCT level) to ensure that data about a particular participant are requested from the correct practice. - Direct contact with GPs may be required to seek further details about particular health related outcomes identified through linkages to coded primary care data. - To enable GPs to be confident in their assessing primary care data about participants registered in their practices, Biobank will need to be able to provide GP practices with up to date listings of UK Biobank participants in their practices, and – where requested – evidence of their consent. - Follow-up in a cohort study needs to be as complete as possible. This is to avoid both unnecessary reduction in size of the cohort (which reduces statistical power of data analyses), and loss-to-follow-up bias (which can cause misleading research results). Part of UK Biobank’s follow-up strategy relies on intermittent re-contact with UK Biobank’s participants to invite them to complete brief questionnaires about health related exposures or outcomes. It is important that UK Biobank have correct and up-to-date participant contact details for this. Although UK Biobank encourage participants to inform UK Biobank when they move, this is not a failsafe method of ensuring the most accurate information. - UK Biobank are committed to ensuring participants continue to be kept aware of progress and developments in UK Biobank, to obtaining their opinions on proposed enhancements to the study, and to providing them with information on how to contact UK Biobank should they have any questions. UK Biobank do this in part through Biobank’s annual newsletter, sent via e-mail and/or regular mail. Clearly, UK Biobank need up to date contact details (especially postal address) to do this. UK Biobank's wish to avoid contacting participants at the wrong address, as this may cause confusion or even occasional distress. - Important potential research questions about the effects of environmental exposures on health require information about current and previous postal address since this allows these exposures to be estimated (from postcodes). These include, for example, valuable studies of the health effects of living close to power cables, aspects of the local environment such as nearby local public amenities and green space, and exposure to background radiation.	UK Biobank’s IT infrastructure ensures that the most up-to-date security technology is used to maintain confidentiality of participant data, with regularly up-dated firewalls, antivirus software and other data encryption protocols to ensure on-going compliance with the Data Protection Act and other regulatory requirements. Periodic penetrance testing, as part of a regular security audits, is carried out by an independent third party to ensure that no data can be accessed by unauthorised individuals. Importantly, the main administrative database, which includes identifiable information on participants (allowing for linkage to health-related records, re-contact for further data collection and keeping participants informed of study progress) is located and maintained separately from the research database (which contains all the data of potential interest for researchers but no data from which any participant could be identified). Access to the administrative database is closely monitored and is strictly limited to a very small number of named staff. Data once processed may be provided to researchers according to the UK Biobank access policy and procedures, which were developed through extensive consultation, including public consultation and consultation with UK Biobank participants. Further detail is given within the Outputs section. Any proposed research project, whether led from an academic institution, a company, a charity, or a collaborative combination of any of these, is only approved for access to data held by UK Biobank (which data can only be used for the purposes of approved research project) following a clear demonstration that the primary aim of the project is to generate results that are for the benefit of the public’s health and for the promotion of health throughout society. Since the UK Biobank resource is based on 500,000 UK-based participants (90% of whom were recruited in England and registered with the English NHS), the public health and health promotion benefits arising from the results of approved research based on the UK Biobank resource will be apply most directly to recipients of health services and social care in England, although many of the benefits will also have broader applicability to populations beyond those of England and the UK.	Access to the resource UK Biobank’s access policy is set out in the Ethics and Governance Framework. Its Access Procedures were prepared during 2010 to 2011 and were the subject of extensive discussion and dialogue with its funders, the REC and the EGC. The Access Procedures were also put out to public consultation, including consultation with UK Biobank participants. In summary: - Every researcher has to register and apply to UK Biobank. If successful, they have to execute UK Biobank's Material Transfer Agreement (MTA) (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Material- Transfer-Agreement.pdf (for information presented to DAAG in the supporting documentation). The application process is the same irrespective of the nature of the applicant and the applicant's location. The MTA governs the legal relationship between UK Biobank and each researcher and, inter alia, ensures that the researcher complies with all the applicable data protection requirements relating to all participant data that they receive. In addition to audit provisions (i.e. UK Biobank is entitled to audit any applicant if it transpires that a transgression has or may have taken place) the MTA contains explicit provisions stating that UK Biobank will bar a transgressing institution from any further use of the resource as well as publicly informing its governing bodies, funders et al ; - The criteria are that the applicant must be a bona fide researcher conducting health-related research in the public interest. Note that the researcher may be based internationally, but that the terms under which that international researcher may access the data is set out within the MTA and are the same as those for a UK researcher. The adjudication of each application is made by UK Biobank’s Access and Scientific teams, with independent ethical advice (from the University of Oxford’s multidisciplinary bioethics research centre, Ethox: http://www.ndph.ox.ac.uk/research/ethox-centre) as well as input from UK Biobank’s independent Ethics and Governance Council, and the whole process is overseen by UK Biobank’s Access Sub Committee (http://www.ukbiobank.ac.uk/access-to-the-resource/), which contains both scientific and legal / ethical expertise; - Any data released to researchers is pseudonymised and encrypted. The researcher is only permitted to use the data for a particular (approved) research project for a particular (approved) time frame after which the researcher must securely destroy the data. The results of the research have to come back to UK Biobank – where they are then accessible by other researchers – and the researcher is obliged to publish their work; - In other words, any data supplied by UK Biobank to a researcher can only be used for the research project in question and for a limited time through UK Biobank’s access procedures (http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf, provided for convenience as document B3). Further, any such data cannot be sub-licensed or made available by researchers to any other party under any circumstances. This is all covered in UK Biobank’s MTA, which is non-negotiable. - Participants have been aware that that the data is made available to researchers anywhere in the world as well as commercial researchers at the time of consent. To demonstrate the information which was made available to each participant: - Our one page screen notes, (also in the resources section of the website) which each participant would view prior to starting the consent process at the assessment centre stated that “Over the coming years, a very wide range of tests would be done on your blood and urine samples by approved researchers from the UK and elsewhere (including commercial companies)”; - Page 8 of the Information Leaflet under the section “Who will be able to use my information and samples” makes it clear that researchers from overseas and commercial researchers can access the resource. The first box in the UK Biobank consent form, also on the website, states that “I have read and understand the Information Leaflet, and have had the opportunity to ask questions”; - In addition on the UK Biobank website on the participants page (http://www.ukbiobank.ac.uk/participants/) includes the following information: Who is using the resource? "UK Biobank is being used by a huge range of scientists from around the world studying lots of different health problems (including those in the US, Australia, France, Spain, Russia & Japan). The resource is open to any bona fide researcher undertaking health related research, wherever they might be. Currently, most researchers are UK-based and working in academia. UK Biobank hopes more researchers from overseas and working in industry will find the resource of value, and use it to improve the health of future generations. You can find out about the research currently under way, and where it is being done, by clicking on our interactive map in the Approved Research section of this web site. The Publications section provides a summary of findings that have been written up in specialist research journals, or been presented to conferences. " - Finally the UK Biobank Approved research page (http://www.ukbiobank.ac.uk/approved-research-2/) contains two maps, one of the UK and one of the whole world. This allows participants to click on a map and see who is doing research in UK Biobank and where they are located in the world.	UK Biobank is a major national health resource, and a registered charity in its own right, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40-69 years in 2006-2010 from across the country to take part in this project. They have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed. This is an increasingly powerful resource that is helping – and will continue over many years to help - scientists discover why some people develop particular diseases and others do not. UK Biobank has joined other world leaders in research at the launch of the Dementias Platform UK, a powerful new tool for studying dementia. The ground-breaking collaboration between industry and academia has been established by the Medical Research Council (MRC) and bolstered by government funding. UK Biobank will be one of the key resources used to help scientists: • Get a better understanding of who is at risk of developing dementia and why the progression of the disease varies from person to person; • Explore the anatomy of the disease to help develop new medicines and enable more accurate diagnosis • Look into how existing drugs which are used to treat other conditions might help to treat the progression of dementia and improve symptoms. All research projects that have already been completed using UK Biobank are required to be displayed at http://www.ukbiobank.ac.uk/approved-research/ . This shows the many projects achieved using UK Biobank. Compliance with HSCIC requirements under The 2014 Care Act • As a research resource, the benefits that will be generated from researchers using UK Biobank are potentially very considerable to the population of the United Kingdom and the National Health Service. UK Biobank’s public service remit, which in due course will benefit the UK population and the health care system, is set out unambiguously on the introductory page of its 2007 Ethics and Governance Framework: “UK Biobank aims to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis, and treatment of illness and the promotion of health throughout society. Lifestyle and environmental information, medical history, physical measurements, and biological samples are to be collected from about 500,000 people aged 40 to 69 at presentation and then, with consent, their health will be followed for many years through medical and other health related records. The biological samples will be stored so that they can be used for a wide range of biochemical and genetic analyses in the future. Scientists have known for many years that our risks of developing different diseases are due to the complex combination of different factors: our lifestyle and environment; our personal susceptibility (genes); and the play of chance (luck). Because UK Biobank will involve thousands of people who develop any particular disease, it will be able to show more reliably than ever before why some people develop that disease while others do not. This should help to find new ways to prevent death and disability from many different conditions. “ • This remit is re-iterated in UK Biobank’s 2012 Access Procedures, which state: “UK Biobank’s purpose is to build a major resource that can support a diverse range of research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”. This intent is paraphrased in the Consent Form (www.ukbiobank.ac.uk/consent) and related information materials as being “health-related” and in the “public interest”. Researchers who apply to use the Resource will be required to explain explicitly how their research project supports this stated purpose.” • In the intervening period between 2007 and 2016, the resource has been built, enhanced and has been open to researchers for just over 3 years. There is simply no other resource currently available, which enables researchers to study such a wide range of diseases with such rich and extensive data. What UK Biobank does is to enable researchers to conduct research in a highly cost-effective manner, rather than duplicate spending and effort on sample and case collection.
WILMINGTON HEALTHCARE	WILMINGTON HEALTHCARE	Hospital Episode Statistics Admitted Patient Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Binley’s, NHiS and Wellards have been part of Wilmington Healthcare for some time. Following some research with their customers and the health and social care sector, it became evident that these brands should come together under one name. As a result, Wilmington Healthcare now represents the bringing together of data intelligence specialists Binley’s, NHiS and Wellards. Wilmington Healthcare is an information intermediary which specialises in applying healthcare data to produce outputs that are used in health and social care to: 1. Raise disease awareness, management and diagnosis through analysing data and publishing reports and tabulations which are available in the public domain 2. Support the commissioning cycle and enhance patient outcome through understanding disease progression and applying to continual service development improvement A directorate within Wilmington Healthcare is the Neurology Commissioning Service (NCS), an official NHS England (Ref: Map of Medicines), niche commissioning support unit. Wilmington Healthcare and its Commissioning Excellence directorate (formerly NCS) have provided aggregated HES data outputs for use in report production and commissioning support. Wilmington Healthcare has used (and wishes to continue to use) record level pseudonymised, and non-sensitive: • HES data since 2008, using data from 2000/01 till current • MHMDS and DIDs data since 2013, using data from 2011/12 till current (not requesting any further MHMDS under this application) Wilmington Healthcare will use the data solely for the following purposes (any other requirement will be subject to a further application) :- PURPOSE 1) Disease Insight Reports (DIRs) • DIRs are reports which publish aggregated, double suppressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data, and seek to: o Increase the appropriate diagnosis of a disease and minimise misdiagnosis o Raise awareness of a specific disease o Analyse the management of disease • For the avoidance of doubt, DIRs will not: o Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of pharmaceutical products o Include any analysis on the impact of commercially available products. An example being pharmaceutical products • Reports are made publically available. For example the “State of the Nation” on Parkinsons used by Newsnight – see example in the Expected Measurable Benefit section • In addition to the publication of national DIRs, local reports are available for sub national health and social care organisations to view data at their level of interest The potential users of DIRs are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Group 6 – General Public Group 7 – Other commercial organisations Wilmington Healthcare have complete editorial control over the DIRs meaning that the reports are developed completely independently of the commissioner of the work. Below is a summary of who the commissioner could be, their editorial influence and the potential publishing channels: Commissioner 1 - Wilmington Healthcare: Pro-Active Internal research reports of interest, independently produced by Wilmington Healthcare and published on the Wilmington Healthcare, NCS or NHS websites, or via hard-copy production Commissioner 2 - Group 1: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. Commissioner 3 - Group 2, 3, 4, 5, 7: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. It is proposed that a legally binding contract between Wilmington Healthcare and the commissioner of the report be signed by both parties. The contract will stipulate what the DIR and consequential aggregated HES, MHMDS or DIDs outputs can and cannot be used for. PURPOSE 2) Q-PASS – Quantis Pathway and Service System Q-PASS is a series of tools that uses aggregated, double supressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data to aid the commissioning cycle. Q-PASS assists health and social care in creating and delivering The Quality, Innovation, Productivity and Prevention (QIPP) priorities , Five Year Forward View Planning, implementation of NICE HTAs, local Five Year Commissioning Plans and Joint Strategic Needs Assessment (between health and social care). Q-PASS allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of a newly implemented pathway and/or service The potential users of Q-PASS are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of QPASS are all of the above groups, the data can only be used for the purposes listed above (with the ultimate beneficiary being the NHS and Social Care). This will be agreed through a contract between Wilmington Healthcare and the 3rd party. HES or DIDs outputs will be used by the ultimate beneficiary, as they have been for five years (one year in the case of DIDs), in the following elements of the Commissioning Cycle: • Analysis Phase: o Dashboards & Analyst - to assess a pathway’s and/or organisation’s performance against similar comparisons and to understand where change could be required to achieve QIPP planning. • Planning Phase: o Dashboards – to communicate with all NHS stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. o Analyst – to identify local health economies which are managing a specific disease effectively. To use this data to quantify what success will look like in terms of reduced inappropriate hospital activity & cost plus decreased comorbid patient disease. o Modeller – to apply predictive modelling to understand the potential impact for patients plus the health and social care system by adopting a clinical pathway or service design which is optimal. • Implementation Phase: o Dashboards – to enable continual communication and education with all NHS stakeholders as to the rationale and requirements for a new clinical pathway or service. • Review Phase: o Dashboards, Analyst, Modeller to review progress on a frequent basis and to make any necessary, close to real-time, changes to the pathway or service to optimise efficiency. Life Science Companies are a user of the aggregated outputs exclusively for the purpose of providing Q-PASS to benefit the health and social care organisations listed in Group 1 in England. Group 5 users will be highly restricted in their use of Q-PASS to ensure aggregated HES, MHMDS or DIDs data outputs are not used for their own commercial purpose such as targeting sales resource. These restrictions are to be underpinned through a signed legal contract between the 3rd party and Wilmington Healthcare. Measures which Wilmington Healthcare recommend are placed on the 3rd party via contractual obligation include but are not limited to: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations listed in Group 1 in England • The system not to be used for commercial purpose • Where appropriate, the system to be governed and resourced by the non-promotional medical department • Where appropriate, an official NHS/industry joint working contract to be put in place • The same aggregated HES or DIDs data outputs to be made available, if requested, to all organisations in Group 1, irrespective of their value to the company • The system only to be provided to a restricted number of named Group 5 users, who have undergone and passed Wilmington Healthcare's HES Protocol training (audited by the NHS Digital) plus the Wilmington Healthcare Data Reuse Protocol (an specific addendum to Group 5 underlining the need for non-commercial reuse) • All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established PMCPA Code of Practice and DH governance on the use of healthcare data by Life Science Companies with health and social care PURPOSE 3) Tabulations Wilmington Healthcare receives unsolicited requests for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on a random basis and as part of wider commissioning projects. The tabulations Wilmington Healthcare wishes to provide are those which are complicated in nature and are required in rapid timeframes to achieve NHS and social care project objectives. Specifically, Wilmington Healthcare does not wish to provide simple tabulations of activity data such as admissions by Trust by ICD10. Instead the purpose for use is to provide tabulations that are complicated in nature, requiring in-depth understanding of the patient pathway and coding practices. Two examples would be: • Unbundling tariffs to understand high cost activity from the core HRG tariff to infer additional information on cost of procedures to provide an actual total charge of a service. • Strategic Clinical Network (SCN) wishing to view, for each of the CCGs within the SCN, an analysis which understands for one particular operation the comorbid conditions patients had pre and post event, how the operation was coded, whether a site of operation was record and the effect on tariff and how these factors influenced outcomes plus cost. The construction of these tabulations are highly dynamic in nature requiring Wilmington Healthcare to work in an iterative fashion to analyse data, assess outputs, refine search and resubmit until the exact answer to the initial problem has been resolved. Frequently, the iterative process can take between five to ten iterations to achieve the required outputs. It is for the combination of the knowledge which Wilmington Healthcare applies to the data and the restraints associated with working remotely that Wilmington Healthcare requires the ability to provide tabulations. For the avoidance of doubt, Tabulations do not: • Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of an individual pharmaceutical products • Include any analysis on the impact of commercially available products. An example being an individual pharmaceutical products The potential users of Tabulations are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of Tabulations are all of the above groups, the ultimate beneficiary is restricted to Group 1 only. This will be agreed through a contract between Wilmington Healthcare and the 3rd party for each tabulation. As part of the contract it will be mandatory for Wilmington Healthcare to publish all tabulations on the Wilmington Healthcare and/or suitable alternative website for public access. Wilmington Healthcare will not solicit requests for Tabulations through web advertising or promotional activity. Wilmington Healthcare will only provide Tabulations based upon inward requests that benefit Group 1. All Tabulation outputs will include double suppression^ and will be in line with the HES Analysis Guide, thus classifying the data as anonymised.	General Processing Activities: All outputs are published at an aggregated level using non-sensitive, non-identifiable HES, MHMDS and DIDs data and in line with the required legislation, guidelines plus policy documentation listed within Wilmington Healthcare's existing Data Re-Use Agreement (DRA) with NHS Digital. This includes suppressing numbers 1 to 5 with either primary or double suppression (* = double suppression is the suppression of any field(s) that would allow imputation of a small number). ^Double Suppression Example: In primary suppression the following is displayed: Total Admissions = 10, Elective Admissions = 9, Non Elective Admissions = * Day Case = 0; in double suppression the following is displayed: Total Admissions = 10; Elective Admissions **, Non Elective Admissions = **; Day Case = 0). This highly secure approach to small number suppression was recognised by NHS Digital's audit team as an area of good practice, stating in their report, “Double suppression of Small Numbers provides extra assurance of security around patient identification.” Wilmington Healthcare match organisation level (aggregated) data from HES to publicly available GP Prescribing, QOF and ODS data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Wilmington Healthcare to third parties and therefore no identifiable data is either available nor can be inferred. This was confirmed by The Information Commissioners Office (ICO), who performed an independent review of Wilmington Healthcare's management of HES data in August 2014. The ICO confirmed, in a published letter, that Wilmington Healthcare neither handles nor creates personal data when using Hospital Episode Statistics and that the ICO was satisfied with Wilmington Healthcare's use of HES data in relation to managing personal data. Pseudonymised HES and DIDs data are securely downloaded via the NHS Digital SEFT server and stored on a secure network drive in one location in England. Record level data are loaded into a data warehouse, on a dedicated private non-external facing server, prior to aggregation into a separate database (both of which are stored independently in the same location in England). The final aggregated, non-sensitive, non-identifiable outputs are uploaded to professionally hosted user facing servers in England. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to the network drive and servers that contain the pseudonymised record level data and aggregated database are restricted to named, fully trained members of Wilmington Healthcare staff with internal audits carried out (and documented) to ensure that only the appropriate, trained personnel within the organisation have access to these datasets. Specific Processing Activities – PURPOSE1: Disease Insight Reports (DIRs) What specifically are Disease Insight Reports? DIRs are an analysis of a disease and/or its management, predominately in secondary care. The methodology of the analysis is based on in-house research undertaken by Wilmington Healthcare using non-sensitive, non-identifiable, record level data. Published outputs will be based on peer reviewed, aggregated, double suppressed^ data and can be exported in the form a PDF, Excel Workbook, written document or equivalent medium available for printing or web publishing. For clarity, Wilmington Healthcare has complete and independent editorial control over the outputs of Disease Insight Reports. Wilmington Healthcare commits to as part of this Purpose statement to: • Publishing all aggregated results, irrespective of outcomes and independent of external influence. • Having outputs reviewed by a member or members of the Wilmington Healthcare advisory group consisting of Medics (GP & Hospital Consultant), Statistician, NHS Health Service Data Analysts, Wilmington Healthcare Insight Consultant, Former head of UK policy and practice adviser for Long Term Conditions at the Royal College of Nursing. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? Please note: Only Wilmington Healthcare will require pseudonymised, record level, non-sensitive, non-identifiable data. Users will view only aggregated, non-sensitive, non-identifiable data which has all suppression rules applied. Record level data is stored separate to aggregated data. There is no possibility of users accessing record level data. DIR’s use patient cohort analysis which requires an aggregation of pseudonymised, non-sensitive, non-identifiable record level data. The ability to be able to see diagnosis, procedures and HRGs by multiple individual episodes at record level is imperative to being able to undertake the analysis for DIRs. The diverse and intricate episode level data is not available through an extract. Specific Processing Activities – PURPOSE 2: Q-PASS What specifically is Q-PASS? Q-PASS is an electronic on and offline commissioning support solution which uses aggregated, double suppressed^ , non-sensitive, non-identifiable record level HES, MHMDS or DIDs data as the outputs. The service is used by registered, authenticated users who have access under licence, over a sustained period, typically of one year. There are three main elements to Q-PASS: • Disease Management Dashboards & Maps (Dashboards) • Disease Management Analyser (Analyst) • Patient Pathway and Service Design Modeller (Modeller) Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract for Q-PASS? Please note: Only Wilmington Healthcare will require access to record level, non-sensitive, non-identifiable record data for the reasons listed below. All other users will receive aggregated, non-sensitive, non-identifiable data which has all suppression rules applied in line with the HES Analysis guide and the guidance within Part 2, section 3.5 of the Data Sharing Framework contract. Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare HES Data Flow v1r0 and HES Protocol v1r0 documents). Pseudonymised non-sensitive, non-identifiable record level data is required so Wilmington Healthcare registered users can: • Comprehend how spells break into episodes at record level, to enable all non-sensitive comorbid conditions and procedures to be rolled into aggregated, non-identifiable, patient cohorts. This allows users to analyse the existing pathway against modelled pathways in detail, to portray disease progression over time at patient cohort level, and to study the impact of poorly managed disease over time. • Establish what HRGs are being applied to each episode in a spell at record level, prior to aggregating into user output data. This allows users to view whether a more efficient tariff or route of treatment could be used. • Provide an aggregated way of demonstrating how the pathway, disease or service being analysed intricately fits with related inter related pathways. For example in diabetes pathway and disease analysis, the areas of obesity, cardiovascular disease, ophthalmology, renal etc. will also require analysing. For each health & social care geography, interrelated pathways are different, meaning the complete spectrum of ICD10, OPCS4 and HRG codes are required. • Analyse patient outcomes such as comorbid disease, procedures and unnecessary hospital activity (admissions, excess bed days, readmissions) at an individual level prior to aggregating into predefined cohorts for use by users in the Modeller system. • Produce a system that allows users to compare at aggregated level one organisation, geography or patient cohort against another with similar characteristics (socio, demographic and ethnicity). This allows users to understand what best practice can look like. The ability to provide feedback in relative real time on the success of a new pathway or new service, is critical to the realisation of the redesign project. This means a monthly breakdown and routine data refresh at record level will be required. How does access and use of the Q-PASS system work? For all user groups access works as follows: • Each user organisation agrees a legal contract with Wilmington Healthcare stipulating terms and conditions (T&Cs) and contains a sub licence with NHS Digital. This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Wilmington Healthcare and NHS Digital o Restrictions on use of data outputs o Duration of contract o Number of users - with an addendum that lists user name and job function o Requirement to publish and reference (where possible) any work which uses the outputs of the HES, MHMDS or DIDs data within Q-PASS o Confirmation that failure to apply with the above will result in Wilmington Healthcare removing the organisation from the approved user list and notifying NHS Digital of the organisation and reason as to removal • On contract signing Wilmington Healthcare provides HES Protocol training to all registered users, which is an on or offline assessment that demonstrates that users know the regulations plus T&Cs relating to use of HES, MHMDS or DIDs data outputs. These users known as Registered, Approved users or RAs also receive an RA Certification. • RAs provided with secure login details (username and password) that they must authenticate to access. • Wilmington Healthcare train authenticated RAs. • RAs use the Q-PASS for the purposes defined the T&Cs. • RA login details to be active for restricted time before expiry and the reissue of new details. • New potential RAs to follow procedure 2 – 4 above. • Wilmington Healthcare Customer Service team responsible for tracking, with the organisation’s commitment, all existing leavers and removing from the system. NB – Wilmington Healthcare will require access to Q-PASS for the purposes of: • Quality assuring updates or changes, and to contribute to on-going Q-PASS improvement • To train and provide on-going support to users • To demonstrate to existing and potential users in all Groups 1,3,4 & 5 • Assist any of the user Groups 1,3,4 & 5 Wilmington Healthcare users, with the exception of NHS Digital registered users, do not have access to record level data; these users only have access to aggregated data via Wilmington Healthcare standard interfaces. This separation is achievable because record level data is stored separate to aggregated data. Access to record level data in Wilmington Healthcare is limited to the small team who are responsible for technical development, data loading and carrying out data aggregation. Specific Processing Activities – PURPOSE 3: Tabulations What specifically are Tabulations? Tabulations are aggregated with small number suppression in line with the HES Analysis Guide and will use HES, MHMDS or DIDs outputs of hospital activity and/or cost at organisational or patient cohort level. They are used by health and social care to quickly find out insight relating to the management of specific diseases or procedures so that effective decisions can be made in real time. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? A tabulation cannot be created without pseudonymised record level data being available to Wilmington Healthcare. Please note: Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare's HES Data Flow v1r0 and HES Protocol v1r0 documents). How does access to and use of Tabulations work? 1. Signed contract by Wilmington Healthcare with the 3rd party stipulating what the Tabulation can and cannot be used for. Copy of signed contract distributed to all parties. 2. Post contract signing Wilmington Healthcare undertake tabulation production 3. Tabulation output peer reviewed and quality assured by Wilmington Healthcare in line with NHS Digital guidance and suppression 4. Tabulation released to 3rd party 5. Tabulation simultaneously published on Wilmington Healthcare's website and any other website NHS Digital require it to be published on.	PURPOSE 1- Disease Insight Reports Published outputs from DIRs are peer reviewed, aggregated data which can either be: • A written report in Word summarising analysed data • An Excel sheet containing aggregated HES data, complete with accompanying commentary • A PDF of either of the above • Any equivalent medium available for printing or web publishing PURPOSE 2 - Q-PASS The following outputs are available within respective elements of Q-PASS. Outputs will be generated by aggregating the data and applying suppression in line with the HES Analysis Guide and other policies as stated within the Data Sharing Agreement (DSA). Secondary suppression^ is also applied to prevent users being able to calculate the number within a suppressed field. Disease Management Dashboards & Maps (Dashboards): • Display dashboards and/or maps produced by disease type and/or geographic area • Display medium can be online and offline (Excel dashboards and Tablet) • PDF export report option available Disease Management Analyser (Analyst) • Excel based system to analyse any aggregated, suppressed, non-sensitive, non-identifiable ICD10/OPCS4 or HRG code for any non-sensitive, non-identifiable fact or dimension in any clinical pathway or organisation • An online system • Export function available Patient Pathway and Service Design Modeller (Modeller) o Offline Excel based system to model the design and cost of one clinical pathway or service against alternatives to establish the most efficient option o Uses the data outputs from Analyst to power the model o Export available PURPOSE 3 - Tabulations Published outputs from tabulations are peer checked, aggregated, double suppressed^ data which is provided: • On an Excel sheet (or PDF version) • For mandatory publishing on the Web	PURPOSE 1: Disease Insight Reports (DIRs) The following groups will be an end beneficiary of DIRs: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients • DIRs aims to assist health and social care through one or several of the following: o Creating a national platform from which to roll out local analysis and improvements o Identifying both current performance and themes around problems affecting individual diseases o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources An example of the benefits DIRs have already achieved is below: A report has been published on the MS Trust and Wilmington Healthcare websites. This report was also shared by Public Health England and the National Clinical Director for Neurology, and been mentioned at all Wilmington Healthcare Neurology commissioning network events during 2015/16 to several hundred senior stakeholders in the NHS. At a neurology commissioning meeting Wilmington Healthcare held on 19 November in Haydock, an MS Nurse Consultant in Salford highlighted how she is now using this data to support their service redesign in MS. One such report (the Neurology Intelligence Report) is the result of close working between a number of organisations including the National Institute for Health Research, which is the research arm of the NHS. The report authors found that in many cases, minor illnesses, which could potentially have been assessed and treated and managed proactively, were responsible for admissions in people with neurological conditions. In addition overall they identified a significant rise in the number of people with neurological conditions who were admitted to hospital, or seen as outpatients. PURPOSE 2: Q-PASS • At a macro level the broad benefits to health and social care of using Q-PASS are: o A reduction in inappropriate hospital activity and cost – avoidable admissions, readmissions, excessive length of stay (LOS) o An overall improvement in patient outcomes – reduced comorbid disease, mortality, LOS, hospital acquired infection; move patient treatment from inpatient to outpatient or primary care o A reduction in the burden on social care – effectively designed clinical pathways and services using Q-PASS stop patients from leaving healthcare and becoming a burden on social care • Q-PASS achieves the benefits by : o Assisting local health and social care environments in identifying where service efficiencies and patient outcomes can be improved before monitoring the impact of any intervention o Studying disease progression, over time, both locally and nationally. Process map patient cohort journeys through data to show the cost of ineffective disease management and the consequences to patients and the social system o Showing healthcare activity and cost, comparing like-for-like organisations and trending data over time o Mapping performance locally and nationally where specialist teams or resources are in place o Providing a reliable evidence baseline for performance to inform key decisions and to enable measurement of impact on the condition o Addressing health inequalities o Providing transferable collaborative service solutions o Measuring success and effectiveness post implementation of a new pathway or service • Examples of where HES outputs being used by health and social care from Q-PASS: o MS Pathway in Hull via the Hull Royal Infirmary used Wilmington Healthcare HES data to understand service need and create a new MS pathway : https://cmscactrims.confex.com/cmscactrims/2014/webprogram/Paper2416.html o Neurology admission and cost analysis across the SE Coast Strategic Clinical Network. This work formed the audit aspect of the commissioning cycle from which service design recommendations were then created. The analysis identified in one CCG alone, for one neurological condition, £237k of potentially avoidable UTI admissions could be saved: http://www.secscn.nhs.uk/files/1114/0360/1253/130614_NHS_South_Kent_Coast_CCG_data_intelligence_report.pdf An example of the benefits Q-PASS has already achieved is below: A life science company in Lancashire has used Q-PASS dashboards to help the CCGs identify issues around variation in the management of type 2 diabetes across the CCGs and their constituent GP practices, develop a new treatment guideline, and support practices with the highest need with training and mentorship to improve their confidence to manage patients appropriately in accordance with the new guidance. PURPOSE 3: Tabulations • Tabulations assist health and social care through rapidly being able to assist with one or several of the following: o Identifying both current performance and identifying problems affecting individual disease management o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources • The following groups will be the end beneficiary and user of Tabulations: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) An example of the benefits the tabulations have already achieved is below: Life Science companies have used HES data tabulations to help NICE better understand the current management of Dupuytren’s contracture, including the range of treatment approaches and the costs associated with these. This has enabled NICE to review their guidance to support a more patient-friendly and cost effective treatment approach to be approved. Wilmington Healthcare's Commissioning Excellence Directorate has used HES data tabulations to enable understanding of patient management in neurology for Redditch and Bromsgrove, Worcestshire and Wyre Forest CCGs. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions and provided recommendations based on consultation with Paris and carers. All recommendations accepted by the 3 CCGs and service transformation is underway. In addition, HES data tabulations have been used to enable understanding of patient management in neurology for Walsall CCG. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions. The CCG is currently utilising the data to enable service transformation.
WILMINGTON HEALTHCARE	WILMINGTON HEALTHCARE	Hospital Episode Statistics Critical Care	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Binley’s, NHiS and Wellards have been part of Wilmington Healthcare for some time. Following some research with their customers and the health and social care sector, it became evident that these brands should come together under one name. As a result, Wilmington Healthcare now represents the bringing together of data intelligence specialists Binley’s, NHiS and Wellards. Wilmington Healthcare is an information intermediary which specialises in applying healthcare data to produce outputs that are used in health and social care to: 1. Raise disease awareness, management and diagnosis through analysing data and publishing reports and tabulations which are available in the public domain 2. Support the commissioning cycle and enhance patient outcome through understanding disease progression and applying to continual service development improvement A directorate within Wilmington Healthcare is the Neurology Commissioning Service (NCS), an official NHS England (Ref: Map of Medicines), niche commissioning support unit. Wilmington Healthcare and its Commissioning Excellence directorate (formerly NCS) have provided aggregated HES data outputs for use in report production and commissioning support. Wilmington Healthcare has used (and wishes to continue to use) record level pseudonymised, and non-sensitive: • HES data since 2008, using data from 2000/01 till current • MHMDS and DIDs data since 2013, using data from 2011/12 till current (not requesting any further MHMDS under this application) Wilmington Healthcare will use the data solely for the following purposes (any other requirement will be subject to a further application) :- PURPOSE 1) Disease Insight Reports (DIRs) • DIRs are reports which publish aggregated, double suppressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data, and seek to: o Increase the appropriate diagnosis of a disease and minimise misdiagnosis o Raise awareness of a specific disease o Analyse the management of disease • For the avoidance of doubt, DIRs will not: o Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of pharmaceutical products o Include any analysis on the impact of commercially available products. An example being pharmaceutical products • Reports are made publically available. For example the “State of the Nation” on Parkinsons used by Newsnight – see example in the Expected Measurable Benefit section • In addition to the publication of national DIRs, local reports are available for sub national health and social care organisations to view data at their level of interest The potential users of DIRs are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Group 6 – General Public Group 7 – Other commercial organisations Wilmington Healthcare have complete editorial control over the DIRs meaning that the reports are developed completely independently of the commissioner of the work. Below is a summary of who the commissioner could be, their editorial influence and the potential publishing channels: Commissioner 1 - Wilmington Healthcare: Pro-Active Internal research reports of interest, independently produced by Wilmington Healthcare and published on the Wilmington Healthcare, NCS or NHS websites, or via hard-copy production Commissioner 2 - Group 1: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. Commissioner 3 - Group 2, 3, 4, 5, 7: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. It is proposed that a legally binding contract between Wilmington Healthcare and the commissioner of the report be signed by both parties. The contract will stipulate what the DIR and consequential aggregated HES, MHMDS or DIDs outputs can and cannot be used for. PURPOSE 2) Q-PASS – Quantis Pathway and Service System Q-PASS is a series of tools that uses aggregated, double supressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data to aid the commissioning cycle. Q-PASS assists health and social care in creating and delivering The Quality, Innovation, Productivity and Prevention (QIPP) priorities , Five Year Forward View Planning, implementation of NICE HTAs, local Five Year Commissioning Plans and Joint Strategic Needs Assessment (between health and social care). Q-PASS allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of a newly implemented pathway and/or service The potential users of Q-PASS are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of QPASS are all of the above groups, the data can only be used for the purposes listed above (with the ultimate beneficiary being the NHS and Social Care). This will be agreed through a contract between Wilmington Healthcare and the 3rd party. HES or DIDs outputs will be used by the ultimate beneficiary, as they have been for five years (one year in the case of DIDs), in the following elements of the Commissioning Cycle: • Analysis Phase: o Dashboards & Analyst - to assess a pathway’s and/or organisation’s performance against similar comparisons and to understand where change could be required to achieve QIPP planning. • Planning Phase: o Dashboards – to communicate with all NHS stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. o Analyst – to identify local health economies which are managing a specific disease effectively. To use this data to quantify what success will look like in terms of reduced inappropriate hospital activity & cost plus decreased comorbid patient disease. o Modeller – to apply predictive modelling to understand the potential impact for patients plus the health and social care system by adopting a clinical pathway or service design which is optimal. • Implementation Phase: o Dashboards – to enable continual communication and education with all NHS stakeholders as to the rationale and requirements for a new clinical pathway or service. • Review Phase: o Dashboards, Analyst, Modeller to review progress on a frequent basis and to make any necessary, close to real-time, changes to the pathway or service to optimise efficiency. Life Science Companies are a user of the aggregated outputs exclusively for the purpose of providing Q-PASS to benefit the health and social care organisations listed in Group 1 in England. Group 5 users will be highly restricted in their use of Q-PASS to ensure aggregated HES, MHMDS or DIDs data outputs are not used for their own commercial purpose such as targeting sales resource. These restrictions are to be underpinned through a signed legal contract between the 3rd party and Wilmington Healthcare. Measures which Wilmington Healthcare recommend are placed on the 3rd party via contractual obligation include but are not limited to: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations listed in Group 1 in England • The system not to be used for commercial purpose • Where appropriate, the system to be governed and resourced by the non-promotional medical department • Where appropriate, an official NHS/industry joint working contract to be put in place • The same aggregated HES or DIDs data outputs to be made available, if requested, to all organisations in Group 1, irrespective of their value to the company • The system only to be provided to a restricted number of named Group 5 users, who have undergone and passed Wilmington Healthcare's HES Protocol training (audited by the NHS Digital) plus the Wilmington Healthcare Data Reuse Protocol (an specific addendum to Group 5 underlining the need for non-commercial reuse) • All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established PMCPA Code of Practice and DH governance on the use of healthcare data by Life Science Companies with health and social care PURPOSE 3) Tabulations Wilmington Healthcare receives unsolicited requests for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on a random basis and as part of wider commissioning projects. The tabulations Wilmington Healthcare wishes to provide are those which are complicated in nature and are required in rapid timeframes to achieve NHS and social care project objectives. Specifically, Wilmington Healthcare does not wish to provide simple tabulations of activity data such as admissions by Trust by ICD10. Instead the purpose for use is to provide tabulations that are complicated in nature, requiring in-depth understanding of the patient pathway and coding practices. Two examples would be: • Unbundling tariffs to understand high cost activity from the core HRG tariff to infer additional information on cost of procedures to provide an actual total charge of a service. • Strategic Clinical Network (SCN) wishing to view, for each of the CCGs within the SCN, an analysis which understands for one particular operation the comorbid conditions patients had pre and post event, how the operation was coded, whether a site of operation was record and the effect on tariff and how these factors influenced outcomes plus cost. The construction of these tabulations are highly dynamic in nature requiring Wilmington Healthcare to work in an iterative fashion to analyse data, assess outputs, refine search and resubmit until the exact answer to the initial problem has been resolved. Frequently, the iterative process can take between five to ten iterations to achieve the required outputs. It is for the combination of the knowledge which Wilmington Healthcare applies to the data and the restraints associated with working remotely that Wilmington Healthcare requires the ability to provide tabulations. For the avoidance of doubt, Tabulations do not: • Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of an individual pharmaceutical products • Include any analysis on the impact of commercially available products. An example being an individual pharmaceutical products The potential users of Tabulations are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of Tabulations are all of the above groups, the ultimate beneficiary is restricted to Group 1 only. This will be agreed through a contract between Wilmington Healthcare and the 3rd party for each tabulation. As part of the contract it will be mandatory for Wilmington Healthcare to publish all tabulations on the Wilmington Healthcare and/or suitable alternative website for public access. Wilmington Healthcare will not solicit requests for Tabulations through web advertising or promotional activity. Wilmington Healthcare will only provide Tabulations based upon inward requests that benefit Group 1. All Tabulation outputs will include double suppression^ and will be in line with the HES Analysis Guide, thus classifying the data as anonymised.	General Processing Activities: All outputs are published at an aggregated level using non-sensitive, non-identifiable HES, MHMDS and DIDs data and in line with the required legislation, guidelines plus policy documentation listed within Wilmington Healthcare's existing Data Re-Use Agreement (DRA) with NHS Digital. This includes suppressing numbers 1 to 5 with either primary or double suppression (* = double suppression is the suppression of any field(s) that would allow imputation of a small number). ^Double Suppression Example: In primary suppression the following is displayed: Total Admissions = 10, Elective Admissions = 9, Non Elective Admissions = * Day Case = 0; in double suppression the following is displayed: Total Admissions = 10; Elective Admissions **, Non Elective Admissions = **; Day Case = 0). This highly secure approach to small number suppression was recognised by NHS Digital's audit team as an area of good practice, stating in their report, “Double suppression of Small Numbers provides extra assurance of security around patient identification.” Wilmington Healthcare match organisation level (aggregated) data from HES to publicly available GP Prescribing, QOF and ODS data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Wilmington Healthcare to third parties and therefore no identifiable data is either available nor can be inferred. This was confirmed by The Information Commissioners Office (ICO), who performed an independent review of Wilmington Healthcare's management of HES data in August 2014. The ICO confirmed, in a published letter, that Wilmington Healthcare neither handles nor creates personal data when using Hospital Episode Statistics and that the ICO was satisfied with Wilmington Healthcare's use of HES data in relation to managing personal data. Pseudonymised HES and DIDs data are securely downloaded via the NHS Digital SEFT server and stored on a secure network drive in one location in England. Record level data are loaded into a data warehouse, on a dedicated private non-external facing server, prior to aggregation into a separate database (both of which are stored independently in the same location in England). The final aggregated, non-sensitive, non-identifiable outputs are uploaded to professionally hosted user facing servers in England. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to the network drive and servers that contain the pseudonymised record level data and aggregated database are restricted to named, fully trained members of Wilmington Healthcare staff with internal audits carried out (and documented) to ensure that only the appropriate, trained personnel within the organisation have access to these datasets. Specific Processing Activities – PURPOSE1: Disease Insight Reports (DIRs) What specifically are Disease Insight Reports? DIRs are an analysis of a disease and/or its management, predominately in secondary care. The methodology of the analysis is based on in-house research undertaken by Wilmington Healthcare using non-sensitive, non-identifiable, record level data. Published outputs will be based on peer reviewed, aggregated, double suppressed^ data and can be exported in the form a PDF, Excel Workbook, written document or equivalent medium available for printing or web publishing. For clarity, Wilmington Healthcare has complete and independent editorial control over the outputs of Disease Insight Reports. Wilmington Healthcare commits to as part of this Purpose statement to: • Publishing all aggregated results, irrespective of outcomes and independent of external influence. • Having outputs reviewed by a member or members of the Wilmington Healthcare advisory group consisting of Medics (GP & Hospital Consultant), Statistician, NHS Health Service Data Analysts, Wilmington Healthcare Insight Consultant, Former head of UK policy and practice adviser for Long Term Conditions at the Royal College of Nursing. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? Please note: Only Wilmington Healthcare will require pseudonymised, record level, non-sensitive, non-identifiable data. Users will view only aggregated, non-sensitive, non-identifiable data which has all suppression rules applied. Record level data is stored separate to aggregated data. There is no possibility of users accessing record level data. DIR’s use patient cohort analysis which requires an aggregation of pseudonymised, non-sensitive, non-identifiable record level data. The ability to be able to see diagnosis, procedures and HRGs by multiple individual episodes at record level is imperative to being able to undertake the analysis for DIRs. The diverse and intricate episode level data is not available through an extract. Specific Processing Activities – PURPOSE 2: Q-PASS What specifically is Q-PASS? Q-PASS is an electronic on and offline commissioning support solution which uses aggregated, double suppressed^ , non-sensitive, non-identifiable record level HES, MHMDS or DIDs data as the outputs. The service is used by registered, authenticated users who have access under licence, over a sustained period, typically of one year. There are three main elements to Q-PASS: • Disease Management Dashboards & Maps (Dashboards) • Disease Management Analyser (Analyst) • Patient Pathway and Service Design Modeller (Modeller) Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract for Q-PASS? Please note: Only Wilmington Healthcare will require access to record level, non-sensitive, non-identifiable record data for the reasons listed below. All other users will receive aggregated, non-sensitive, non-identifiable data which has all suppression rules applied in line with the HES Analysis guide and the guidance within Part 2, section 3.5 of the Data Sharing Framework contract. Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare HES Data Flow v1r0 and HES Protocol v1r0 documents). Pseudonymised non-sensitive, non-identifiable record level data is required so Wilmington Healthcare registered users can: • Comprehend how spells break into episodes at record level, to enable all non-sensitive comorbid conditions and procedures to be rolled into aggregated, non-identifiable, patient cohorts. This allows users to analyse the existing pathway against modelled pathways in detail, to portray disease progression over time at patient cohort level, and to study the impact of poorly managed disease over time. • Establish what HRGs are being applied to each episode in a spell at record level, prior to aggregating into user output data. This allows users to view whether a more efficient tariff or route of treatment could be used. • Provide an aggregated way of demonstrating how the pathway, disease or service being analysed intricately fits with related inter related pathways. For example in diabetes pathway and disease analysis, the areas of obesity, cardiovascular disease, ophthalmology, renal etc. will also require analysing. For each health & social care geography, interrelated pathways are different, meaning the complete spectrum of ICD10, OPCS4 and HRG codes are required. • Analyse patient outcomes such as comorbid disease, procedures and unnecessary hospital activity (admissions, excess bed days, readmissions) at an individual level prior to aggregating into predefined cohorts for use by users in the Modeller system. • Produce a system that allows users to compare at aggregated level one organisation, geography or patient cohort against another with similar characteristics (socio, demographic and ethnicity). This allows users to understand what best practice can look like. The ability to provide feedback in relative real time on the success of a new pathway or new service, is critical to the realisation of the redesign project. This means a monthly breakdown and routine data refresh at record level will be required. How does access and use of the Q-PASS system work? For all user groups access works as follows: • Each user organisation agrees a legal contract with Wilmington Healthcare stipulating terms and conditions (T&Cs) and contains a sub licence with NHS Digital. This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Wilmington Healthcare and NHS Digital o Restrictions on use of data outputs o Duration of contract o Number of users - with an addendum that lists user name and job function o Requirement to publish and reference (where possible) any work which uses the outputs of the HES, MHMDS or DIDs data within Q-PASS o Confirmation that failure to apply with the above will result in Wilmington Healthcare removing the organisation from the approved user list and notifying NHS Digital of the organisation and reason as to removal • On contract signing Wilmington Healthcare provides HES Protocol training to all registered users, which is an on or offline assessment that demonstrates that users know the regulations plus T&Cs relating to use of HES, MHMDS or DIDs data outputs. These users known as Registered, Approved users or RAs also receive an RA Certification. • RAs provided with secure login details (username and password) that they must authenticate to access. • Wilmington Healthcare train authenticated RAs. • RAs use the Q-PASS for the purposes defined the T&Cs. • RA login details to be active for restricted time before expiry and the reissue of new details. • New potential RAs to follow procedure 2 – 4 above. • Wilmington Healthcare Customer Service team responsible for tracking, with the organisation’s commitment, all existing leavers and removing from the system. NB – Wilmington Healthcare will require access to Q-PASS for the purposes of: • Quality assuring updates or changes, and to contribute to on-going Q-PASS improvement • To train and provide on-going support to users • To demonstrate to existing and potential users in all Groups 1,3,4 & 5 • Assist any of the user Groups 1,3,4 & 5 Wilmington Healthcare users, with the exception of NHS Digital registered users, do not have access to record level data; these users only have access to aggregated data via Wilmington Healthcare standard interfaces. This separation is achievable because record level data is stored separate to aggregated data. Access to record level data in Wilmington Healthcare is limited to the small team who are responsible for technical development, data loading and carrying out data aggregation. Specific Processing Activities – PURPOSE 3: Tabulations What specifically are Tabulations? Tabulations are aggregated with small number suppression in line with the HES Analysis Guide and will use HES, MHMDS or DIDs outputs of hospital activity and/or cost at organisational or patient cohort level. They are used by health and social care to quickly find out insight relating to the management of specific diseases or procedures so that effective decisions can be made in real time. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? A tabulation cannot be created without pseudonymised record level data being available to Wilmington Healthcare. Please note: Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare's HES Data Flow v1r0 and HES Protocol v1r0 documents). How does access to and use of Tabulations work? 1. Signed contract by Wilmington Healthcare with the 3rd party stipulating what the Tabulation can and cannot be used for. Copy of signed contract distributed to all parties. 2. Post contract signing Wilmington Healthcare undertake tabulation production 3. Tabulation output peer reviewed and quality assured by Wilmington Healthcare in line with NHS Digital guidance and suppression 4. Tabulation released to 3rd party 5. Tabulation simultaneously published on Wilmington Healthcare's website and any other website NHS Digital require it to be published on.	PURPOSE 1- Disease Insight Reports Published outputs from DIRs are peer reviewed, aggregated data which can either be: • A written report in Word summarising analysed data • An Excel sheet containing aggregated HES data, complete with accompanying commentary • A PDF of either of the above • Any equivalent medium available for printing or web publishing PURPOSE 2 - Q-PASS The following outputs are available within respective elements of Q-PASS. Outputs will be generated by aggregating the data and applying suppression in line with the HES Analysis Guide and other policies as stated within the Data Sharing Agreement (DSA). Secondary suppression^ is also applied to prevent users being able to calculate the number within a suppressed field. Disease Management Dashboards & Maps (Dashboards): • Display dashboards and/or maps produced by disease type and/or geographic area • Display medium can be online and offline (Excel dashboards and Tablet) • PDF export report option available Disease Management Analyser (Analyst) • Excel based system to analyse any aggregated, suppressed, non-sensitive, non-identifiable ICD10/OPCS4 or HRG code for any non-sensitive, non-identifiable fact or dimension in any clinical pathway or organisation • An online system • Export function available Patient Pathway and Service Design Modeller (Modeller) o Offline Excel based system to model the design and cost of one clinical pathway or service against alternatives to establish the most efficient option o Uses the data outputs from Analyst to power the model o Export available PURPOSE 3 - Tabulations Published outputs from tabulations are peer checked, aggregated, double suppressed^ data which is provided: • On an Excel sheet (or PDF version) • For mandatory publishing on the Web	PURPOSE 1: Disease Insight Reports (DIRs) The following groups will be an end beneficiary of DIRs: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients • DIRs aims to assist health and social care through one or several of the following: o Creating a national platform from which to roll out local analysis and improvements o Identifying both current performance and themes around problems affecting individual diseases o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources An example of the benefits DIRs have already achieved is below: A report has been published on the MS Trust and Wilmington Healthcare websites. This report was also shared by Public Health England and the National Clinical Director for Neurology, and been mentioned at all Wilmington Healthcare Neurology commissioning network events during 2015/16 to several hundred senior stakeholders in the NHS. At a neurology commissioning meeting Wilmington Healthcare held on 19 November in Haydock, an MS Nurse Consultant in Salford highlighted how she is now using this data to support their service redesign in MS. One such report (the Neurology Intelligence Report) is the result of close working between a number of organisations including the National Institute for Health Research, which is the research arm of the NHS. The report authors found that in many cases, minor illnesses, which could potentially have been assessed and treated and managed proactively, were responsible for admissions in people with neurological conditions. In addition overall they identified a significant rise in the number of people with neurological conditions who were admitted to hospital, or seen as outpatients. PURPOSE 2: Q-PASS • At a macro level the broad benefits to health and social care of using Q-PASS are: o A reduction in inappropriate hospital activity and cost – avoidable admissions, readmissions, excessive length of stay (LOS) o An overall improvement in patient outcomes – reduced comorbid disease, mortality, LOS, hospital acquired infection; move patient treatment from inpatient to outpatient or primary care o A reduction in the burden on social care – effectively designed clinical pathways and services using Q-PASS stop patients from leaving healthcare and becoming a burden on social care • Q-PASS achieves the benefits by : o Assisting local health and social care environments in identifying where service efficiencies and patient outcomes can be improved before monitoring the impact of any intervention o Studying disease progression, over time, both locally and nationally. Process map patient cohort journeys through data to show the cost of ineffective disease management and the consequences to patients and the social system o Showing healthcare activity and cost, comparing like-for-like organisations and trending data over time o Mapping performance locally and nationally where specialist teams or resources are in place o Providing a reliable evidence baseline for performance to inform key decisions and to enable measurement of impact on the condition o Addressing health inequalities o Providing transferable collaborative service solutions o Measuring success and effectiveness post implementation of a new pathway or service • Examples of where HES outputs being used by health and social care from Q-PASS: o MS Pathway in Hull via the Hull Royal Infirmary used Wilmington Healthcare HES data to understand service need and create a new MS pathway : https://cmscactrims.confex.com/cmscactrims/2014/webprogram/Paper2416.html o Neurology admission and cost analysis across the SE Coast Strategic Clinical Network. This work formed the audit aspect of the commissioning cycle from which service design recommendations were then created. The analysis identified in one CCG alone, for one neurological condition, £237k of potentially avoidable UTI admissions could be saved: http://www.secscn.nhs.uk/files/1114/0360/1253/130614_NHS_South_Kent_Coast_CCG_data_intelligence_report.pdf An example of the benefits Q-PASS has already achieved is below: A life science company in Lancashire has used Q-PASS dashboards to help the CCGs identify issues around variation in the management of type 2 diabetes across the CCGs and their constituent GP practices, develop a new treatment guideline, and support practices with the highest need with training and mentorship to improve their confidence to manage patients appropriately in accordance with the new guidance. PURPOSE 3: Tabulations • Tabulations assist health and social care through rapidly being able to assist with one or several of the following: o Identifying both current performance and identifying problems affecting individual disease management o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources • The following groups will be the end beneficiary and user of Tabulations: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) An example of the benefits the tabulations have already achieved is below: Life Science companies have used HES data tabulations to help NICE better understand the current management of Dupuytren’s contracture, including the range of treatment approaches and the costs associated with these. This has enabled NICE to review their guidance to support a more patient-friendly and cost effective treatment approach to be approved. Wilmington Healthcare's Commissioning Excellence Directorate has used HES data tabulations to enable understanding of patient management in neurology for Redditch and Bromsgrove, Worcestshire and Wyre Forest CCGs. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions and provided recommendations based on consultation with Paris and carers. All recommendations accepted by the 3 CCGs and service transformation is underway. In addition, HES data tabulations have been used to enable understanding of patient management in neurology for Walsall CCG. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions. The CCG is currently utilising the data to enable service transformation.
WILMINGTON HEALTHCARE	WILMINGTON HEALTHCARE	Hospital Episode Statistics Accident and Emergency	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Binley’s, NHiS and Wellards have been part of Wilmington Healthcare for some time. Following some research with their customers and the health and social care sector, it became evident that these brands should come together under one name. As a result, Wilmington Healthcare now represents the bringing together of data intelligence specialists Binley’s, NHiS and Wellards. Wilmington Healthcare is an information intermediary which specialises in applying healthcare data to produce outputs that are used in health and social care to: 1. Raise disease awareness, management and diagnosis through analysing data and publishing reports and tabulations which are available in the public domain 2. Support the commissioning cycle and enhance patient outcome through understanding disease progression and applying to continual service development improvement A directorate within Wilmington Healthcare is the Neurology Commissioning Service (NCS), an official NHS England (Ref: Map of Medicines), niche commissioning support unit. Wilmington Healthcare and its Commissioning Excellence directorate (formerly NCS) have provided aggregated HES data outputs for use in report production and commissioning support. Wilmington Healthcare has used (and wishes to continue to use) record level pseudonymised, and non-sensitive: • HES data since 2008, using data from 2000/01 till current • MHMDS and DIDs data since 2013, using data from 2011/12 till current (not requesting any further MHMDS under this application) Wilmington Healthcare will use the data solely for the following purposes (any other requirement will be subject to a further application) :- PURPOSE 1) Disease Insight Reports (DIRs) • DIRs are reports which publish aggregated, double suppressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data, and seek to: o Increase the appropriate diagnosis of a disease and minimise misdiagnosis o Raise awareness of a specific disease o Analyse the management of disease • For the avoidance of doubt, DIRs will not: o Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of pharmaceutical products o Include any analysis on the impact of commercially available products. An example being pharmaceutical products • Reports are made publically available. For example the “State of the Nation” on Parkinsons used by Newsnight – see example in the Expected Measurable Benefit section • In addition to the publication of national DIRs, local reports are available for sub national health and social care organisations to view data at their level of interest The potential users of DIRs are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Group 6 – General Public Group 7 – Other commercial organisations Wilmington Healthcare have complete editorial control over the DIRs meaning that the reports are developed completely independently of the commissioner of the work. Below is a summary of who the commissioner could be, their editorial influence and the potential publishing channels: Commissioner 1 - Wilmington Healthcare: Pro-Active Internal research reports of interest, independently produced by Wilmington Healthcare and published on the Wilmington Healthcare, NCS or NHS websites, or via hard-copy production Commissioner 2 - Group 1: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. Commissioner 3 - Group 2, 3, 4, 5, 7: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. It is proposed that a legally binding contract between Wilmington Healthcare and the commissioner of the report be signed by both parties. The contract will stipulate what the DIR and consequential aggregated HES, MHMDS or DIDs outputs can and cannot be used for. PURPOSE 2) Q-PASS – Quantis Pathway and Service System Q-PASS is a series of tools that uses aggregated, double supressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data to aid the commissioning cycle. Q-PASS assists health and social care in creating and delivering The Quality, Innovation, Productivity and Prevention (QIPP) priorities , Five Year Forward View Planning, implementation of NICE HTAs, local Five Year Commissioning Plans and Joint Strategic Needs Assessment (between health and social care). Q-PASS allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of a newly implemented pathway and/or service The potential users of Q-PASS are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of QPASS are all of the above groups, the data can only be used for the purposes listed above (with the ultimate beneficiary being the NHS and Social Care). This will be agreed through a contract between Wilmington Healthcare and the 3rd party. HES or DIDs outputs will be used by the ultimate beneficiary, as they have been for five years (one year in the case of DIDs), in the following elements of the Commissioning Cycle: • Analysis Phase: o Dashboards & Analyst - to assess a pathway’s and/or organisation’s performance against similar comparisons and to understand where change could be required to achieve QIPP planning. • Planning Phase: o Dashboards – to communicate with all NHS stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. o Analyst – to identify local health economies which are managing a specific disease effectively. To use this data to quantify what success will look like in terms of reduced inappropriate hospital activity & cost plus decreased comorbid patient disease. o Modeller – to apply predictive modelling to understand the potential impact for patients plus the health and social care system by adopting a clinical pathway or service design which is optimal. • Implementation Phase: o Dashboards – to enable continual communication and education with all NHS stakeholders as to the rationale and requirements for a new clinical pathway or service. • Review Phase: o Dashboards, Analyst, Modeller to review progress on a frequent basis and to make any necessary, close to real-time, changes to the pathway or service to optimise efficiency. Life Science Companies are a user of the aggregated outputs exclusively for the purpose of providing Q-PASS to benefit the health and social care organisations listed in Group 1 in England. Group 5 users will be highly restricted in their use of Q-PASS to ensure aggregated HES, MHMDS or DIDs data outputs are not used for their own commercial purpose such as targeting sales resource. These restrictions are to be underpinned through a signed legal contract between the 3rd party and Wilmington Healthcare. Measures which Wilmington Healthcare recommend are placed on the 3rd party via contractual obligation include but are not limited to: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations listed in Group 1 in England • The system not to be used for commercial purpose • Where appropriate, the system to be governed and resourced by the non-promotional medical department • Where appropriate, an official NHS/industry joint working contract to be put in place • The same aggregated HES or DIDs data outputs to be made available, if requested, to all organisations in Group 1, irrespective of their value to the company • The system only to be provided to a restricted number of named Group 5 users, who have undergone and passed Wilmington Healthcare's HES Protocol training (audited by the NHS Digital) plus the Wilmington Healthcare Data Reuse Protocol (an specific addendum to Group 5 underlining the need for non-commercial reuse) • All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established PMCPA Code of Practice and DH governance on the use of healthcare data by Life Science Companies with health and social care PURPOSE 3) Tabulations Wilmington Healthcare receives unsolicited requests for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on a random basis and as part of wider commissioning projects. The tabulations Wilmington Healthcare wishes to provide are those which are complicated in nature and are required in rapid timeframes to achieve NHS and social care project objectives. Specifically, Wilmington Healthcare does not wish to provide simple tabulations of activity data such as admissions by Trust by ICD10. Instead the purpose for use is to provide tabulations that are complicated in nature, requiring in-depth understanding of the patient pathway and coding practices. Two examples would be: • Unbundling tariffs to understand high cost activity from the core HRG tariff to infer additional information on cost of procedures to provide an actual total charge of a service. • Strategic Clinical Network (SCN) wishing to view, for each of the CCGs within the SCN, an analysis which understands for one particular operation the comorbid conditions patients had pre and post event, how the operation was coded, whether a site of operation was record and the effect on tariff and how these factors influenced outcomes plus cost. The construction of these tabulations are highly dynamic in nature requiring Wilmington Healthcare to work in an iterative fashion to analyse data, assess outputs, refine search and resubmit until the exact answer to the initial problem has been resolved. Frequently, the iterative process can take between five to ten iterations to achieve the required outputs. It is for the combination of the knowledge which Wilmington Healthcare applies to the data and the restraints associated with working remotely that Wilmington Healthcare requires the ability to provide tabulations. For the avoidance of doubt, Tabulations do not: • Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of an individual pharmaceutical products • Include any analysis on the impact of commercially available products. An example being an individual pharmaceutical products The potential users of Tabulations are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of Tabulations are all of the above groups, the ultimate beneficiary is restricted to Group 1 only. This will be agreed through a contract between Wilmington Healthcare and the 3rd party for each tabulation. As part of the contract it will be mandatory for Wilmington Healthcare to publish all tabulations on the Wilmington Healthcare and/or suitable alternative website for public access. Wilmington Healthcare will not solicit requests for Tabulations through web advertising or promotional activity. Wilmington Healthcare will only provide Tabulations based upon inward requests that benefit Group 1. All Tabulation outputs will include double suppression^ and will be in line with the HES Analysis Guide, thus classifying the data as anonymised.	General Processing Activities: All outputs are published at an aggregated level using non-sensitive, non-identifiable HES, MHMDS and DIDs data and in line with the required legislation, guidelines plus policy documentation listed within Wilmington Healthcare's existing Data Re-Use Agreement (DRA) with NHS Digital. This includes suppressing numbers 1 to 5 with either primary or double suppression (* = double suppression is the suppression of any field(s) that would allow imputation of a small number). ^Double Suppression Example: In primary suppression the following is displayed: Total Admissions = 10, Elective Admissions = 9, Non Elective Admissions = * Day Case = 0; in double suppression the following is displayed: Total Admissions = 10; Elective Admissions **, Non Elective Admissions = **; Day Case = 0). This highly secure approach to small number suppression was recognised by NHS Digital's audit team as an area of good practice, stating in their report, “Double suppression of Small Numbers provides extra assurance of security around patient identification.” Wilmington Healthcare match organisation level (aggregated) data from HES to publicly available GP Prescribing, QOF and ODS data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Wilmington Healthcare to third parties and therefore no identifiable data is either available nor can be inferred. This was confirmed by The Information Commissioners Office (ICO), who performed an independent review of Wilmington Healthcare's management of HES data in August 2014. The ICO confirmed, in a published letter, that Wilmington Healthcare neither handles nor creates personal data when using Hospital Episode Statistics and that the ICO was satisfied with Wilmington Healthcare's use of HES data in relation to managing personal data. Pseudonymised HES and DIDs data are securely downloaded via the NHS Digital SEFT server and stored on a secure network drive in one location in England. Record level data are loaded into a data warehouse, on a dedicated private non-external facing server, prior to aggregation into a separate database (both of which are stored independently in the same location in England). The final aggregated, non-sensitive, non-identifiable outputs are uploaded to professionally hosted user facing servers in England. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to the network drive and servers that contain the pseudonymised record level data and aggregated database are restricted to named, fully trained members of Wilmington Healthcare staff with internal audits carried out (and documented) to ensure that only the appropriate, trained personnel within the organisation have access to these datasets. Specific Processing Activities – PURPOSE1: Disease Insight Reports (DIRs) What specifically are Disease Insight Reports? DIRs are an analysis of a disease and/or its management, predominately in secondary care. The methodology of the analysis is based on in-house research undertaken by Wilmington Healthcare using non-sensitive, non-identifiable, record level data. Published outputs will be based on peer reviewed, aggregated, double suppressed^ data and can be exported in the form a PDF, Excel Workbook, written document or equivalent medium available for printing or web publishing. For clarity, Wilmington Healthcare has complete and independent editorial control over the outputs of Disease Insight Reports. Wilmington Healthcare commits to as part of this Purpose statement to: • Publishing all aggregated results, irrespective of outcomes and independent of external influence. • Having outputs reviewed by a member or members of the Wilmington Healthcare advisory group consisting of Medics (GP & Hospital Consultant), Statistician, NHS Health Service Data Analysts, Wilmington Healthcare Insight Consultant, Former head of UK policy and practice adviser for Long Term Conditions at the Royal College of Nursing. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? Please note: Only Wilmington Healthcare will require pseudonymised, record level, non-sensitive, non-identifiable data. Users will view only aggregated, non-sensitive, non-identifiable data which has all suppression rules applied. Record level data is stored separate to aggregated data. There is no possibility of users accessing record level data. DIR’s use patient cohort analysis which requires an aggregation of pseudonymised, non-sensitive, non-identifiable record level data. The ability to be able to see diagnosis, procedures and HRGs by multiple individual episodes at record level is imperative to being able to undertake the analysis for DIRs. The diverse and intricate episode level data is not available through an extract. Specific Processing Activities – PURPOSE 2: Q-PASS What specifically is Q-PASS? Q-PASS is an electronic on and offline commissioning support solution which uses aggregated, double suppressed^ , non-sensitive, non-identifiable record level HES, MHMDS or DIDs data as the outputs. The service is used by registered, authenticated users who have access under licence, over a sustained period, typically of one year. There are three main elements to Q-PASS: • Disease Management Dashboards & Maps (Dashboards) • Disease Management Analyser (Analyst) • Patient Pathway and Service Design Modeller (Modeller) Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract for Q-PASS? Please note: Only Wilmington Healthcare will require access to record level, non-sensitive, non-identifiable record data for the reasons listed below. All other users will receive aggregated, non-sensitive, non-identifiable data which has all suppression rules applied in line with the HES Analysis guide and the guidance within Part 2, section 3.5 of the Data Sharing Framework contract. Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare HES Data Flow v1r0 and HES Protocol v1r0 documents). Pseudonymised non-sensitive, non-identifiable record level data is required so Wilmington Healthcare registered users can: • Comprehend how spells break into episodes at record level, to enable all non-sensitive comorbid conditions and procedures to be rolled into aggregated, non-identifiable, patient cohorts. This allows users to analyse the existing pathway against modelled pathways in detail, to portray disease progression over time at patient cohort level, and to study the impact of poorly managed disease over time. • Establish what HRGs are being applied to each episode in a spell at record level, prior to aggregating into user output data. This allows users to view whether a more efficient tariff or route of treatment could be used. • Provide an aggregated way of demonstrating how the pathway, disease or service being analysed intricately fits with related inter related pathways. For example in diabetes pathway and disease analysis, the areas of obesity, cardiovascular disease, ophthalmology, renal etc. will also require analysing. For each health & social care geography, interrelated pathways are different, meaning the complete spectrum of ICD10, OPCS4 and HRG codes are required. • Analyse patient outcomes such as comorbid disease, procedures and unnecessary hospital activity (admissions, excess bed days, readmissions) at an individual level prior to aggregating into predefined cohorts for use by users in the Modeller system. • Produce a system that allows users to compare at aggregated level one organisation, geography or patient cohort against another with similar characteristics (socio, demographic and ethnicity). This allows users to understand what best practice can look like. The ability to provide feedback in relative real time on the success of a new pathway or new service, is critical to the realisation of the redesign project. This means a monthly breakdown and routine data refresh at record level will be required. How does access and use of the Q-PASS system work? For all user groups access works as follows: • Each user organisation agrees a legal contract with Wilmington Healthcare stipulating terms and conditions (T&Cs) and contains a sub licence with NHS Digital. This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Wilmington Healthcare and NHS Digital o Restrictions on use of data outputs o Duration of contract o Number of users - with an addendum that lists user name and job function o Requirement to publish and reference (where possible) any work which uses the outputs of the HES, MHMDS or DIDs data within Q-PASS o Confirmation that failure to apply with the above will result in Wilmington Healthcare removing the organisation from the approved user list and notifying NHS Digital of the organisation and reason as to removal • On contract signing Wilmington Healthcare provides HES Protocol training to all registered users, which is an on or offline assessment that demonstrates that users know the regulations plus T&Cs relating to use of HES, MHMDS or DIDs data outputs. These users known as Registered, Approved users or RAs also receive an RA Certification. • RAs provided with secure login details (username and password) that they must authenticate to access. • Wilmington Healthcare train authenticated RAs. • RAs use the Q-PASS for the purposes defined the T&Cs. • RA login details to be active for restricted time before expiry and the reissue of new details. • New potential RAs to follow procedure 2 – 4 above. • Wilmington Healthcare Customer Service team responsible for tracking, with the organisation’s commitment, all existing leavers and removing from the system. NB – Wilmington Healthcare will require access to Q-PASS for the purposes of: • Quality assuring updates or changes, and to contribute to on-going Q-PASS improvement • To train and provide on-going support to users • To demonstrate to existing and potential users in all Groups 1,3,4 & 5 • Assist any of the user Groups 1,3,4 & 5 Wilmington Healthcare users, with the exception of NHS Digital registered users, do not have access to record level data; these users only have access to aggregated data via Wilmington Healthcare standard interfaces. This separation is achievable because record level data is stored separate to aggregated data. Access to record level data in Wilmington Healthcare is limited to the small team who are responsible for technical development, data loading and carrying out data aggregation. Specific Processing Activities – PURPOSE 3: Tabulations What specifically are Tabulations? Tabulations are aggregated with small number suppression in line with the HES Analysis Guide and will use HES, MHMDS or DIDs outputs of hospital activity and/or cost at organisational or patient cohort level. They are used by health and social care to quickly find out insight relating to the management of specific diseases or procedures so that effective decisions can be made in real time. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? A tabulation cannot be created without pseudonymised record level data being available to Wilmington Healthcare. Please note: Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare's HES Data Flow v1r0 and HES Protocol v1r0 documents). How does access to and use of Tabulations work? 1. Signed contract by Wilmington Healthcare with the 3rd party stipulating what the Tabulation can and cannot be used for. Copy of signed contract distributed to all parties. 2. Post contract signing Wilmington Healthcare undertake tabulation production 3. Tabulation output peer reviewed and quality assured by Wilmington Healthcare in line with NHS Digital guidance and suppression 4. Tabulation released to 3rd party 5. Tabulation simultaneously published on Wilmington Healthcare's website and any other website NHS Digital require it to be published on.	PURPOSE 1- Disease Insight Reports Published outputs from DIRs are peer reviewed, aggregated data which can either be: • A written report in Word summarising analysed data • An Excel sheet containing aggregated HES data, complete with accompanying commentary • A PDF of either of the above • Any equivalent medium available for printing or web publishing PURPOSE 2 - Q-PASS The following outputs are available within respective elements of Q-PASS. Outputs will be generated by aggregating the data and applying suppression in line with the HES Analysis Guide and other policies as stated within the Data Sharing Agreement (DSA). Secondary suppression^ is also applied to prevent users being able to calculate the number within a suppressed field. Disease Management Dashboards & Maps (Dashboards): • Display dashboards and/or maps produced by disease type and/or geographic area • Display medium can be online and offline (Excel dashboards and Tablet) • PDF export report option available Disease Management Analyser (Analyst) • Excel based system to analyse any aggregated, suppressed, non-sensitive, non-identifiable ICD10/OPCS4 or HRG code for any non-sensitive, non-identifiable fact or dimension in any clinical pathway or organisation • An online system • Export function available Patient Pathway and Service Design Modeller (Modeller) o Offline Excel based system to model the design and cost of one clinical pathway or service against alternatives to establish the most efficient option o Uses the data outputs from Analyst to power the model o Export available PURPOSE 3 - Tabulations Published outputs from tabulations are peer checked, aggregated, double suppressed^ data which is provided: • On an Excel sheet (or PDF version) • For mandatory publishing on the Web	PURPOSE 1: Disease Insight Reports (DIRs) The following groups will be an end beneficiary of DIRs: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients • DIRs aims to assist health and social care through one or several of the following: o Creating a national platform from which to roll out local analysis and improvements o Identifying both current performance and themes around problems affecting individual diseases o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources An example of the benefits DIRs have already achieved is below: A report has been published on the MS Trust and Wilmington Healthcare websites. This report was also shared by Public Health England and the National Clinical Director for Neurology, and been mentioned at all Wilmington Healthcare Neurology commissioning network events during 2015/16 to several hundred senior stakeholders in the NHS. At a neurology commissioning meeting Wilmington Healthcare held on 19 November in Haydock, an MS Nurse Consultant in Salford highlighted how she is now using this data to support their service redesign in MS. One such report (the Neurology Intelligence Report) is the result of close working between a number of organisations including the National Institute for Health Research, which is the research arm of the NHS. The report authors found that in many cases, minor illnesses, which could potentially have been assessed and treated and managed proactively, were responsible for admissions in people with neurological conditions. In addition overall they identified a significant rise in the number of people with neurological conditions who were admitted to hospital, or seen as outpatients. PURPOSE 2: Q-PASS • At a macro level the broad benefits to health and social care of using Q-PASS are: o A reduction in inappropriate hospital activity and cost – avoidable admissions, readmissions, excessive length of stay (LOS) o An overall improvement in patient outcomes – reduced comorbid disease, mortality, LOS, hospital acquired infection; move patient treatment from inpatient to outpatient or primary care o A reduction in the burden on social care – effectively designed clinical pathways and services using Q-PASS stop patients from leaving healthcare and becoming a burden on social care • Q-PASS achieves the benefits by : o Assisting local health and social care environments in identifying where service efficiencies and patient outcomes can be improved before monitoring the impact of any intervention o Studying disease progression, over time, both locally and nationally. Process map patient cohort journeys through data to show the cost of ineffective disease management and the consequences to patients and the social system o Showing healthcare activity and cost, comparing like-for-like organisations and trending data over time o Mapping performance locally and nationally where specialist teams or resources are in place o Providing a reliable evidence baseline for performance to inform key decisions and to enable measurement of impact on the condition o Addressing health inequalities o Providing transferable collaborative service solutions o Measuring success and effectiveness post implementation of a new pathway or service • Examples of where HES outputs being used by health and social care from Q-PASS: o MS Pathway in Hull via the Hull Royal Infirmary used Wilmington Healthcare HES data to understand service need and create a new MS pathway : https://cmscactrims.confex.com/cmscactrims/2014/webprogram/Paper2416.html o Neurology admission and cost analysis across the SE Coast Strategic Clinical Network. This work formed the audit aspect of the commissioning cycle from which service design recommendations were then created. The analysis identified in one CCG alone, for one neurological condition, £237k of potentially avoidable UTI admissions could be saved: http://www.secscn.nhs.uk/files/1114/0360/1253/130614_NHS_South_Kent_Coast_CCG_data_intelligence_report.pdf An example of the benefits Q-PASS has already achieved is below: A life science company in Lancashire has used Q-PASS dashboards to help the CCGs identify issues around variation in the management of type 2 diabetes across the CCGs and their constituent GP practices, develop a new treatment guideline, and support practices with the highest need with training and mentorship to improve their confidence to manage patients appropriately in accordance with the new guidance. PURPOSE 3: Tabulations • Tabulations assist health and social care through rapidly being able to assist with one or several of the following: o Identifying both current performance and identifying problems affecting individual disease management o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources • The following groups will be the end beneficiary and user of Tabulations: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) An example of the benefits the tabulations have already achieved is below: Life Science companies have used HES data tabulations to help NICE better understand the current management of Dupuytren’s contracture, including the range of treatment approaches and the costs associated with these. This has enabled NICE to review their guidance to support a more patient-friendly and cost effective treatment approach to be approved. Wilmington Healthcare's Commissioning Excellence Directorate has used HES data tabulations to enable understanding of patient management in neurology for Redditch and Bromsgrove, Worcestshire and Wyre Forest CCGs. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions and provided recommendations based on consultation with Paris and carers. All recommendations accepted by the 3 CCGs and service transformation is underway. In addition, HES data tabulations have been used to enable understanding of patient management in neurology for Walsall CCG. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions. The CCG is currently utilising the data to enable service transformation.
WILMINGTON HEALTHCARE	WILMINGTON HEALTHCARE	Hospital Episode Statistics Outpatients	Anonymised - ICO code compliant	Non Sensitive	Health and Social Care Act 2012	Ongoing	N	Binley’s, NHiS and Wellards have been part of Wilmington Healthcare for some time. Following some research with their customers and the health and social care sector, it became evident that these brands should come together under one name. As a result, Wilmington Healthcare now represents the bringing together of data intelligence specialists Binley’s, NHiS and Wellards. Wilmington Healthcare is an information intermediary which specialises in applying healthcare data to produce outputs that are used in health and social care to: 1. Raise disease awareness, management and diagnosis through analysing data and publishing reports and tabulations which are available in the public domain 2. Support the commissioning cycle and enhance patient outcome through understanding disease progression and applying to continual service development improvement A directorate within Wilmington Healthcare is the Neurology Commissioning Service (NCS), an official NHS England (Ref: Map of Medicines), niche commissioning support unit. Wilmington Healthcare and its Commissioning Excellence directorate (formerly NCS) have provided aggregated HES data outputs for use in report production and commissioning support. Wilmington Healthcare has used (and wishes to continue to use) record level pseudonymised, and non-sensitive: • HES data since 2008, using data from 2000/01 till current • MHMDS and DIDs data since 2013, using data from 2011/12 till current (not requesting any further MHMDS under this application) Wilmington Healthcare will use the data solely for the following purposes (any other requirement will be subject to a further application) :- PURPOSE 1) Disease Insight Reports (DIRs) • DIRs are reports which publish aggregated, double suppressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data, and seek to: o Increase the appropriate diagnosis of a disease and minimise misdiagnosis o Raise awareness of a specific disease o Analyse the management of disease • For the avoidance of doubt, DIRs will not: o Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of pharmaceutical products o Include any analysis on the impact of commercially available products. An example being pharmaceutical products • Reports are made publically available. For example the “State of the Nation” on Parkinsons used by Newsnight – see example in the Expected Measurable Benefit section • In addition to the publication of national DIRs, local reports are available for sub national health and social care organisations to view data at their level of interest The potential users of DIRs are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Group 6 – General Public Group 7 – Other commercial organisations Wilmington Healthcare have complete editorial control over the DIRs meaning that the reports are developed completely independently of the commissioner of the work. Below is a summary of who the commissioner could be, their editorial influence and the potential publishing channels: Commissioner 1 - Wilmington Healthcare: Pro-Active Internal research reports of interest, independently produced by Wilmington Healthcare and published on the Wilmington Healthcare, NCS or NHS websites, or via hard-copy production Commissioner 2 - Group 1: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. Commissioner 3 - Group 2, 3, 4, 5, 7: Research reports in which Wilmington Healthcare has complete editorial control, without external influence. All reports published on either the Wilmington Healthcare, NCS, NHS or other website or via hard-copy production. It is proposed that a legally binding contract between Wilmington Healthcare and the commissioner of the report be signed by both parties. The contract will stipulate what the DIR and consequential aggregated HES, MHMDS or DIDs outputs can and cannot be used for. PURPOSE 2) Q-PASS – Quantis Pathway and Service System Q-PASS is a series of tools that uses aggregated, double supressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data to aid the commissioning cycle. Q-PASS assists health and social care in creating and delivering The Quality, Innovation, Productivity and Prevention (QIPP) priorities , Five Year Forward View Planning, implementation of NICE HTAs, local Five Year Commissioning Plans and Joint Strategic Needs Assessment (between health and social care). Q-PASS allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of a newly implemented pathway and/or service The potential users of Q-PASS are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of QPASS are all of the above groups, the data can only be used for the purposes listed above (with the ultimate beneficiary being the NHS and Social Care). This will be agreed through a contract between Wilmington Healthcare and the 3rd party. HES or DIDs outputs will be used by the ultimate beneficiary, as they have been for five years (one year in the case of DIDs), in the following elements of the Commissioning Cycle: • Analysis Phase: o Dashboards & Analyst - to assess a pathway’s and/or organisation’s performance against similar comparisons and to understand where change could be required to achieve QIPP planning. • Planning Phase: o Dashboards – to communicate with all NHS stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. o Analyst – to identify local health economies which are managing a specific disease effectively. To use this data to quantify what success will look like in terms of reduced inappropriate hospital activity & cost plus decreased comorbid patient disease. o Modeller – to apply predictive modelling to understand the potential impact for patients plus the health and social care system by adopting a clinical pathway or service design which is optimal. • Implementation Phase: o Dashboards – to enable continual communication and education with all NHS stakeholders as to the rationale and requirements for a new clinical pathway or service. • Review Phase: o Dashboards, Analyst, Modeller to review progress on a frequent basis and to make any necessary, close to real-time, changes to the pathway or service to optimise efficiency. Life Science Companies are a user of the aggregated outputs exclusively for the purpose of providing Q-PASS to benefit the health and social care organisations listed in Group 1 in England. Group 5 users will be highly restricted in their use of Q-PASS to ensure aggregated HES, MHMDS or DIDs data outputs are not used for their own commercial purpose such as targeting sales resource. These restrictions are to be underpinned through a signed legal contract between the 3rd party and Wilmington Healthcare. Measures which Wilmington Healthcare recommend are placed on the 3rd party via contractual obligation include but are not limited to: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations listed in Group 1 in England • The system not to be used for commercial purpose • Where appropriate, the system to be governed and resourced by the non-promotional medical department • Where appropriate, an official NHS/industry joint working contract to be put in place • The same aggregated HES or DIDs data outputs to be made available, if requested, to all organisations in Group 1, irrespective of their value to the company • The system only to be provided to a restricted number of named Group 5 users, who have undergone and passed Wilmington Healthcare's HES Protocol training (audited by the NHS Digital) plus the Wilmington Healthcare Data Reuse Protocol (an specific addendum to Group 5 underlining the need for non-commercial reuse) • All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established PMCPA Code of Practice and DH governance on the use of healthcare data by Life Science Companies with health and social care PURPOSE 3) Tabulations Wilmington Healthcare receives unsolicited requests for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on a random basis and as part of wider commissioning projects. The tabulations Wilmington Healthcare wishes to provide are those which are complicated in nature and are required in rapid timeframes to achieve NHS and social care project objectives. Specifically, Wilmington Healthcare does not wish to provide simple tabulations of activity data such as admissions by Trust by ICD10. Instead the purpose for use is to provide tabulations that are complicated in nature, requiring in-depth understanding of the patient pathway and coding practices. Two examples would be: • Unbundling tariffs to understand high cost activity from the core HRG tariff to infer additional information on cost of procedures to provide an actual total charge of a service. • Strategic Clinical Network (SCN) wishing to view, for each of the CCGs within the SCN, an analysis which understands for one particular operation the comorbid conditions patients had pre and post event, how the operation was coded, whether a site of operation was record and the effect on tariff and how these factors influenced outcomes plus cost. The construction of these tabulations are highly dynamic in nature requiring Wilmington Healthcare to work in an iterative fashion to analyse data, assess outputs, refine search and resubmit until the exact answer to the initial problem has been resolved. Frequently, the iterative process can take between five to ten iterations to achieve the required outputs. It is for the combination of the knowledge which Wilmington Healthcare applies to the data and the restraints associated with working remotely that Wilmington Healthcare requires the ability to provide tabulations. For the avoidance of doubt, Tabulations do not: • Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of an individual pharmaceutical products • Include any analysis on the impact of commercially available products. An example being an individual pharmaceutical products The potential users of Tabulations are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of Tabulations are all of the above groups, the ultimate beneficiary is restricted to Group 1 only. This will be agreed through a contract between Wilmington Healthcare and the 3rd party for each tabulation. As part of the contract it will be mandatory for Wilmington Healthcare to publish all tabulations on the Wilmington Healthcare and/or suitable alternative website for public access. Wilmington Healthcare will not solicit requests for Tabulations through web advertising or promotional activity. Wilmington Healthcare will only provide Tabulations based upon inward requests that benefit Group 1. All Tabulation outputs will include double suppression^ and will be in line with the HES Analysis Guide, thus classifying the data as anonymised.	General Processing Activities: All outputs are published at an aggregated level using non-sensitive, non-identifiable HES, MHMDS and DIDs data and in line with the required legislation, guidelines plus policy documentation listed within Wilmington Healthcare's existing Data Re-Use Agreement (DRA) with NHS Digital. This includes suppressing numbers 1 to 5 with either primary or double suppression (* = double suppression is the suppression of any field(s) that would allow imputation of a small number). ^Double Suppression Example: In primary suppression the following is displayed: Total Admissions = 10, Elective Admissions = 9, Non Elective Admissions = * Day Case = 0; in double suppression the following is displayed: Total Admissions = 10; Elective Admissions **, Non Elective Admissions = **; Day Case = 0). This highly secure approach to small number suppression was recognised by NHS Digital's audit team as an area of good practice, stating in their report, “Double suppression of Small Numbers provides extra assurance of security around patient identification.” Wilmington Healthcare match organisation level (aggregated) data from HES to publicly available GP Prescribing, QOF and ODS data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by Wilmington Healthcare to third parties and therefore no identifiable data is either available nor can be inferred. This was confirmed by The Information Commissioners Office (ICO), who performed an independent review of Wilmington Healthcare's management of HES data in August 2014. The ICO confirmed, in a published letter, that Wilmington Healthcare neither handles nor creates personal data when using Hospital Episode Statistics and that the ICO was satisfied with Wilmington Healthcare's use of HES data in relation to managing personal data. Pseudonymised HES and DIDs data are securely downloaded via the NHS Digital SEFT server and stored on a secure network drive in one location in England. Record level data are loaded into a data warehouse, on a dedicated private non-external facing server, prior to aggregation into a separate database (both of which are stored independently in the same location in England). The final aggregated, non-sensitive, non-identifiable outputs are uploaded to professionally hosted user facing servers in England. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to the network drive and servers that contain the pseudonymised record level data and aggregated database are restricted to named, fully trained members of Wilmington Healthcare staff with internal audits carried out (and documented) to ensure that only the appropriate, trained personnel within the organisation have access to these datasets. Specific Processing Activities – PURPOSE1: Disease Insight Reports (DIRs) What specifically are Disease Insight Reports? DIRs are an analysis of a disease and/or its management, predominately in secondary care. The methodology of the analysis is based on in-house research undertaken by Wilmington Healthcare using non-sensitive, non-identifiable, record level data. Published outputs will be based on peer reviewed, aggregated, double suppressed^ data and can be exported in the form a PDF, Excel Workbook, written document or equivalent medium available for printing or web publishing. For clarity, Wilmington Healthcare has complete and independent editorial control over the outputs of Disease Insight Reports. Wilmington Healthcare commits to as part of this Purpose statement to: • Publishing all aggregated results, irrespective of outcomes and independent of external influence. • Having outputs reviewed by a member or members of the Wilmington Healthcare advisory group consisting of Medics (GP & Hospital Consultant), Statistician, NHS Health Service Data Analysts, Wilmington Healthcare Insight Consultant, Former head of UK policy and practice adviser for Long Term Conditions at the Royal College of Nursing. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? Please note: Only Wilmington Healthcare will require pseudonymised, record level, non-sensitive, non-identifiable data. Users will view only aggregated, non-sensitive, non-identifiable data which has all suppression rules applied. Record level data is stored separate to aggregated data. There is no possibility of users accessing record level data. DIR’s use patient cohort analysis which requires an aggregation of pseudonymised, non-sensitive, non-identifiable record level data. The ability to be able to see diagnosis, procedures and HRGs by multiple individual episodes at record level is imperative to being able to undertake the analysis for DIRs. The diverse and intricate episode level data is not available through an extract. Specific Processing Activities – PURPOSE 2: Q-PASS What specifically is Q-PASS? Q-PASS is an electronic on and offline commissioning support solution which uses aggregated, double suppressed^ , non-sensitive, non-identifiable record level HES, MHMDS or DIDs data as the outputs. The service is used by registered, authenticated users who have access under licence, over a sustained period, typically of one year. There are three main elements to Q-PASS: • Disease Management Dashboards & Maps (Dashboards) • Disease Management Analyser (Analyst) • Patient Pathway and Service Design Modeller (Modeller) Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract for Q-PASS? Please note: Only Wilmington Healthcare will require access to record level, non-sensitive, non-identifiable record data for the reasons listed below. All other users will receive aggregated, non-sensitive, non-identifiable data which has all suppression rules applied in line with the HES Analysis guide and the guidance within Part 2, section 3.5 of the Data Sharing Framework contract. Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare HES Data Flow v1r0 and HES Protocol v1r0 documents). Pseudonymised non-sensitive, non-identifiable record level data is required so Wilmington Healthcare registered users can: • Comprehend how spells break into episodes at record level, to enable all non-sensitive comorbid conditions and procedures to be rolled into aggregated, non-identifiable, patient cohorts. This allows users to analyse the existing pathway against modelled pathways in detail, to portray disease progression over time at patient cohort level, and to study the impact of poorly managed disease over time. • Establish what HRGs are being applied to each episode in a spell at record level, prior to aggregating into user output data. This allows users to view whether a more efficient tariff or route of treatment could be used. • Provide an aggregated way of demonstrating how the pathway, disease or service being analysed intricately fits with related inter related pathways. For example in diabetes pathway and disease analysis, the areas of obesity, cardiovascular disease, ophthalmology, renal etc. will also require analysing. For each health & social care geography, interrelated pathways are different, meaning the complete spectrum of ICD10, OPCS4 and HRG codes are required. • Analyse patient outcomes such as comorbid disease, procedures and unnecessary hospital activity (admissions, excess bed days, readmissions) at an individual level prior to aggregating into predefined cohorts for use by users in the Modeller system. • Produce a system that allows users to compare at aggregated level one organisation, geography or patient cohort against another with similar characteristics (socio, demographic and ethnicity). This allows users to understand what best practice can look like. The ability to provide feedback in relative real time on the success of a new pathway or new service, is critical to the realisation of the redesign project. This means a monthly breakdown and routine data refresh at record level will be required. How does access and use of the Q-PASS system work? For all user groups access works as follows: • Each user organisation agrees a legal contract with Wilmington Healthcare stipulating terms and conditions (T&Cs) and contains a sub licence with NHS Digital. This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between Wilmington Healthcare and NHS Digital o Restrictions on use of data outputs o Duration of contract o Number of users - with an addendum that lists user name and job function o Requirement to publish and reference (where possible) any work which uses the outputs of the HES, MHMDS or DIDs data within Q-PASS o Confirmation that failure to apply with the above will result in Wilmington Healthcare removing the organisation from the approved user list and notifying NHS Digital of the organisation and reason as to removal • On contract signing Wilmington Healthcare provides HES Protocol training to all registered users, which is an on or offline assessment that demonstrates that users know the regulations plus T&Cs relating to use of HES, MHMDS or DIDs data outputs. These users known as Registered, Approved users or RAs also receive an RA Certification. • RAs provided with secure login details (username and password) that they must authenticate to access. • Wilmington Healthcare train authenticated RAs. • RAs use the Q-PASS for the purposes defined the T&Cs. • RA login details to be active for restricted time before expiry and the reissue of new details. • New potential RAs to follow procedure 2 – 4 above. • Wilmington Healthcare Customer Service team responsible for tracking, with the organisation’s commitment, all existing leavers and removing from the system. NB – Wilmington Healthcare will require access to Q-PASS for the purposes of: • Quality assuring updates or changes, and to contribute to on-going Q-PASS improvement • To train and provide on-going support to users • To demonstrate to existing and potential users in all Groups 1,3,4 & 5 • Assist any of the user Groups 1,3,4 & 5 Wilmington Healthcare users, with the exception of NHS Digital registered users, do not have access to record level data; these users only have access to aggregated data via Wilmington Healthcare standard interfaces. This separation is achievable because record level data is stored separate to aggregated data. Access to record level data in Wilmington Healthcare is limited to the small team who are responsible for technical development, data loading and carrying out data aggregation. Specific Processing Activities – PURPOSE 3: Tabulations What specifically are Tabulations? Tabulations are aggregated with small number suppression in line with the HES Analysis Guide and will use HES, MHMDS or DIDs outputs of hospital activity and/or cost at organisational or patient cohort level. They are used by health and social care to quickly find out insight relating to the management of specific diseases or procedures so that effective decisions can be made in real time. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES or DIDs extract? A tabulation cannot be created without pseudonymised record level data being available to Wilmington Healthcare. Please note: Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the Wilmington Healthcare's HES Data Flow v1r0 and HES Protocol v1r0 documents). How does access to and use of Tabulations work? 1. Signed contract by Wilmington Healthcare with the 3rd party stipulating what the Tabulation can and cannot be used for. Copy of signed contract distributed to all parties. 2. Post contract signing Wilmington Healthcare undertake tabulation production 3. Tabulation output peer reviewed and quality assured by Wilmington Healthcare in line with NHS Digital guidance and suppression 4. Tabulation released to 3rd party 5. Tabulation simultaneously published on Wilmington Healthcare's website and any other website NHS Digital require it to be published on.	PURPOSE 1- Disease Insight Reports Published outputs from DIRs are peer reviewed, aggregated data which can either be: • A written report in Word summarising analysed data • An Excel sheet containing aggregated HES data, complete with accompanying commentary • A PDF of either of the above • Any equivalent medium available for printing or web publishing PURPOSE 2 - Q-PASS The following outputs are available within respective elements of Q-PASS. Outputs will be generated by aggregating the data and applying suppression in line with the HES Analysis Guide and other policies as stated within the Data Sharing Agreement (DSA). Secondary suppression^ is also applied to prevent users being able to calculate the number within a suppressed field. Disease Management Dashboards & Maps (Dashboards): • Display dashboards and/or maps produced by disease type and/or geographic area • Display medium can be online and offline (Excel dashboards and Tablet) • PDF export report option available Disease Management Analyser (Analyst) • Excel based system to analyse any aggregated, suppressed, non-sensitive, non-identifiable ICD10/OPCS4 or HRG code for any non-sensitive, non-identifiable fact or dimension in any clinical pathway or organisation • An online system • Export function available Patient Pathway and Service Design Modeller (Modeller) o Offline Excel based system to model the design and cost of one clinical pathway or service against alternatives to establish the most efficient option o Uses the data outputs from Analyst to power the model o Export available PURPOSE 3 - Tabulations Published outputs from tabulations are peer checked, aggregated, double suppressed^ data which is provided: • On an Excel sheet (or PDF version) • For mandatory publishing on the Web	PURPOSE 1: Disease Insight Reports (DIRs) The following groups will be an end beneficiary of DIRs: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients • DIRs aims to assist health and social care through one or several of the following: o Creating a national platform from which to roll out local analysis and improvements o Identifying both current performance and themes around problems affecting individual diseases o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources An example of the benefits DIRs have already achieved is below: A report has been published on the MS Trust and Wilmington Healthcare websites. This report was also shared by Public Health England and the National Clinical Director for Neurology, and been mentioned at all Wilmington Healthcare Neurology commissioning network events during 2015/16 to several hundred senior stakeholders in the NHS. At a neurology commissioning meeting Wilmington Healthcare held on 19 November in Haydock, an MS Nurse Consultant in Salford highlighted how she is now using this data to support their service redesign in MS. One such report (the Neurology Intelligence Report) is the result of close working between a number of organisations including the National Institute for Health Research, which is the research arm of the NHS. The report authors found that in many cases, minor illnesses, which could potentially have been assessed and treated and managed proactively, were responsible for admissions in people with neurological conditions. In addition overall they identified a significant rise in the number of people with neurological conditions who were admitted to hospital, or seen as outpatients. PURPOSE 2: Q-PASS • At a macro level the broad benefits to health and social care of using Q-PASS are: o A reduction in inappropriate hospital activity and cost – avoidable admissions, readmissions, excessive length of stay (LOS) o An overall improvement in patient outcomes – reduced comorbid disease, mortality, LOS, hospital acquired infection; move patient treatment from inpatient to outpatient or primary care o A reduction in the burden on social care – effectively designed clinical pathways and services using Q-PASS stop patients from leaving healthcare and becoming a burden on social care • Q-PASS achieves the benefits by : o Assisting local health and social care environments in identifying where service efficiencies and patient outcomes can be improved before monitoring the impact of any intervention o Studying disease progression, over time, both locally and nationally. Process map patient cohort journeys through data to show the cost of ineffective disease management and the consequences to patients and the social system o Showing healthcare activity and cost, comparing like-for-like organisations and trending data over time o Mapping performance locally and nationally where specialist teams or resources are in place o Providing a reliable evidence baseline for performance to inform key decisions and to enable measurement of impact on the condition o Addressing health inequalities o Providing transferable collaborative service solutions o Measuring success and effectiveness post implementation of a new pathway or service • Examples of where HES outputs being used by health and social care from Q-PASS: o MS Pathway in Hull via the Hull Royal Infirmary used Wilmington Healthcare HES data to understand service need and create a new MS pathway : https://cmscactrims.confex.com/cmscactrims/2014/webprogram/Paper2416.html o Neurology admission and cost analysis across the SE Coast Strategic Clinical Network. This work formed the audit aspect of the commissioning cycle from which service design recommendations were then created. The analysis identified in one CCG alone, for one neurological condition, £237k of potentially avoidable UTI admissions could be saved: http://www.secscn.nhs.uk/files/1114/0360/1253/130614_NHS_South_Kent_Coast_CCG_data_intelligence_report.pdf An example of the benefits Q-PASS has already achieved is below: A life science company in Lancashire has used Q-PASS dashboards to help the CCGs identify issues around variation in the management of type 2 diabetes across the CCGs and their constituent GP practices, develop a new treatment guideline, and support practices with the highest need with training and mentorship to improve their confidence to manage patients appropriately in accordance with the new guidance. PURPOSE 3: Tabulations • Tabulations assist health and social care through rapidly being able to assist with one or several of the following: o Identifying both current performance and identifying problems affecting individual disease management o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources • The following groups will be the end beneficiary and user of Tabulations: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) An example of the benefits the tabulations have already achieved is below: Life Science companies have used HES data tabulations to help NICE better understand the current management of Dupuytren’s contracture, including the range of treatment approaches and the costs associated with these. This has enabled NICE to review their guidance to support a more patient-friendly and cost effective treatment approach to be approved. Wilmington Healthcare's Commissioning Excellence Directorate has used HES data tabulations to enable understanding of patient management in neurology for Redditch and Bromsgrove, Worcestshire and Wyre Forest CCGs. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions and provided recommendations based on consultation with Paris and carers. All recommendations accepted by the 3 CCGs and service transformation is underway. In addition, HES data tabulations have been used to enable understanding of patient management in neurology for Walsall CCG. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions. The CCG is currently utilising the data to enable service transformation.
WOLFSON INSTITUTE OF PREVENTIVE MEDICINE	WOLFSON INSTITUTE OF PREVENTIVE MEDICINE	MRIS - Cause of Death Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to Wolfson Institute of Preventative Medicine will be used only for the approved Medical Research Project MR710.
WOLFSON INSTITUTE OF PREVENTIVE MEDICINE	WOLFSON INSTITUTE OF PREVENTIVE MEDICINE	MRIS - Cohort Event Notification Report	Identifiable	Sensitive	Section 251 approval is in place for the flow of identifiable data	Ongoing	Y	The data supplied by the NHS IC to Wolfson Institute of Preventative Medicine will be used only for the approved Medical Research Project MR710.